UNIQ+ projects
Projects available for entry in 2024
As part of your UNIQ+ Research Internship, you will be working on a project under the supervision of academic staff from our community of world-leading researchers.
Places for UNIQ+ internships will be distributed across a wide range of subject areas with up to 130 places available in total. The application form will ask you to select at least one and up to three preferred projects that you are interested in working on. It is expected that the projects you select would usually be in a similar subject area.
The next sections of this page provide details of the projects available this year, categorised by research area. Many of our projects are open to those studying undergraduate degrees in a broad range of subjects, however you should read the project descriptions carefully and check to see if a project has any specific entry requirements before applying.
Full instructions for completing the application form can be found in our Application Guide.
How are places distributed?
If you are successful, we will try to match your interests to available projects and supervisors. We will also match you to funding on the basis of your interests, project/supervisor availability, and closest match with the eligibility criteria, in order to maximise the number of places we can offer.
Please note that we will not always be able to meet your preferences for a project/supervisor, but we will try our best to do this wherever possible. Only projects that are matched to successful applicants will run this year.
Wellcome Biomedical Vacation Scholarships
We intend to offer up to eight Wellcome-funded UNIQ+ placements to individuals who meet the eligibility criteria and apply for the projects in the medical sciences that are eligible for Wellcome funding (this will be indicated in the project description where applicable).
The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions).
Google DeepMind
We intend to offer up to ten Google DeepMind-funded UNIQ+ placements to individuals who meet the eligibility criteria and apply for projects focussed on artificial intelligence / machine-learning that are eligible for Google DeepMind funding (this will be indicated in the project description where applicable).
The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September.
Confirming externally-funded places
If you are successful with your application to one of these projects and your place is funded by an external sponsor, your offer will make this clear and provide all the contractual details, including how you will be paid and any additional activities.
Please note that there may be some amendments to the published information for internships funded by external sponsors in line with specific agreements with these funders. These amendments will be published as soon as they are available.
Projects in Humanities and Social Sciences are offered by the following departments:
Archaeology
Archaeology 01
Reconstructing past farming practices in western Germany
Supervisor
Dr Amy Styring
Theme
Archaeology
Description
The chemistry of plants reflects the soil and environmental conditions in which they grow and can therefore act as a ‘time capsule’ of how crops like cereals were grown in the past.
This project will examine the chemistry of wheat and barley grains preserved on archaeological sites in western Germany to explore how farming practices (eg manuring inputs) changed through time, from the Neolithic to Roman period (5500 BC – 400 AD). This will provide the comparative framework to reconstruct how farming in this region compared to elsewhere in Europe and to the subsequent medieval period (500 – 1200 AD) and help to understand the long-term sustainability of these agricultural practices over thousands of years.
Outcomes
You will experience using and taking images on a light microscope, becoming proficient in laboratory skills to determine the chemistry of organic materials and analysing data using R.
Entry requirements
You should have knowledge of archaeology or a science-related subject such as (but not limited to) chemistry, biology, physics, earth sciences and environmental studies.
Archaeology 02
Quantifying the volcanic ash particle shapes of large magnitude eruptions
Supervisor
Professor Victoria Smith
Theme
Archaeology
Description
Large volcanic eruptions disperse volcanic ash (tephra) hundreds to thousands of kilometres from source. At these locations that are distal to the volcanic vent, the deposits form layers that are millimetres to decimetres in thickness but occasionally they are so dilute and fine that they cannot be identified by eye. Recent studies have suggested that this thickness in the distal environment is dependent on particle shape. We have an impressive volcanic record from a lake in Japan, Lake Suigetsu, that records more than 50 explosive eruptions as both visible and non visible (termed cryptotephra) layers.
This project will involve imaging volcanic glass shards from various tephra layers that are from a couple of volcanoes in Japan. The aim is to establish whether there is a relationship between glass particle shape and tephra thickness.
Outcomes
You will gain experience using and taking images on a scanning electron microscope, and processing the images in MatLab or Python. The data will be able to assess what particle shape characteristics control dispersal distance. You may have the opportunity to contribute to a publication.
Entry requirements
You should have, or be studying towards, a degree in earth sciences, geography or another related field. You should also have an interest in volcanoes, and working with computer code.
Archaeology 03
Dating archaeological cave and palaeoenvironmental sites using volcanic ash layers
Supervisor
Professor Victoria Smith
Theme
Archaeology
Description
Many Middle Palaeolithic archaeological sites have poor chronologies and thus, it is not possible to directly compare technologies and changes between sites. We have sediment samples from a suite of cave sites in Morocco that likely span the last 200,000 years. These caves are rich in archaeological remains and dating these records is crucial to understand human evolution, technological change, and migration in this area.
Fortunately, ash from volcanoes in the Canary Islands and Azores has been found in some sequences across the region, including marine records off the coast. Eruptions from these volcanoes are well dated and locating their ash in the cave sediments provides an age and can be used to correlate the archaeological records.
The aim of this project is to find volcanic ash layers in the sedimentary records using density separating techniques in the laboratory, and link the layers to dated eruptions by analysing the composition of the volcanic glass shards and comparing these data to our glass composition database for the volcanic islands.
Outcomes
You will become proficient in the laboratory skills to identify volcanic ash layers that are not visible in sediments. You will also experience analysing volcanic ash on an electron microprobe, and understand correlation to known and dated volcanic eruptions.
Entry requirements
You should have a degree in archaeology, earth sciences, geography or another closely related field. An interest in volcanoes would be highly beneficial.
Digital Humanities
Digital Humanities 01
Hoax! or the curious tale of William Stukeley and the false history of Roman Britain
Supervisor
Dr Jack Orchard
Theme
Digital humanities
Description
Discover the truth inside a tangled web of lies and forgeries as you explore the letters and relationships behind an antiquarian hoax which went unchallenged for almost a century.
In 1747 William Stukeley (1687–1765), the founder of the British Society of Antiquaries, and one of the 18th century’s most celebrated historians of ancient Britain, received a letter from a fellow antiquarian in the Netherlands, one Charles Bertram (1723–1765). This letter described an incredible new discovery, The Description of Britain, a 15th century manuscript which completely subverted contemporary understandings of the geography of Roman Britain. Excited by this incredible discovery, Stukeley took Bertram under his wing, and promoted his new friend to the antiquarian world. The only problem, The Description was a forgery.
Gain unprecedented access to the correspondence between Bertram and Stukeley as you work with Electronic Enlightenment, Bodleian Archives and Manuscripts, and the Bodleian Imaging Studio to image, transcribe and catalogue the letters.
Outcomes
Working with colleagues across the Bodleian, you will:
- produce an item level catalogue for MS Eng let b. 2, the Stukeley-Bertram letters, learning how to describe letters within the library catalogue and in MARCO, our overarching online catalogue;
- visit the Bodleian Imaging Studio and Bodleian Digital Library to see the process by which a manuscript is converted into a high-quality image ready for publication in the Digital Bodleian interface;
- get a tutorial from the Electronic Enlightenment Content Editor on the transcription of eighteenth-century handwriting for publication online, including learning about mark-up and metadata;
- learn how to research and format biographies in Electronic Enlightenment, and write biographical entries for Stukeley and Bertram, with the support and guidance of our Content Editor;
- get a tutorial on how Electronic Enlightenment deals with locations from our Technical Editor, and build database entries for the places associated with the Stukeley-Bertram collection; and
- write a blog post at the end of the placement describing the Stukeley-Bertram affair and drawing on the letters you have been working with, to be published in the Electronic Enlightenment Blog and circulated through Bodleian and Oxford University Press communications channels.
Entry requirements
You will be IT literate with basic skills (web searching, text editing), but the training associated with the placement itself will include introductions to XML and HTML mark-up, Regular Expressions and working with databases.
You should have, or be studying, a degree in any discipline or background.
Digital Humanities 02
Sustainable digital scholarship: defining the state of the art
Supervisor
Dr Mark McKerracher
Theme
Digital humanities
Description
In Humanities disciplines, the advent of digital research methods has introduced a plethora of ever-changing opportunities and challenges. Innovations in, for example, big data, digital mapping, text-encoding, open-access, and multimedia events and publications are transforming how Humanities research is conducted and disseminated – broadening access and intellectual horizons. But the digital world is subject to constant flux, so it is difficult for researchers to keep abreast of new technologies, practices and pitfalls; meanwhile, web-based resources are prone to become ephemeral or obsolete.
On these shifting sands – and in view of complementary ‘sustainability’ concerns around privacy, security, and environmental impact – how can we define ‘best practice’, for achieving sustainability in digital research?
In this internship, you will delve into digital humanities literature, track down online case studies for comparative study, and work first-hand with Oxford’s Sustainable Digital Scholarship (SDS) team on research data migrations.
Outcomes
You will produce a report on your findings, and publish this (plus any supporting data, media, etc.) on the SDS platform. You will also deliver a research presentation at the Centre for Digital Scholarship at the Bodleian Libraries.
Entry requirements
Students from any degree discipline may apply, but the project is likely to be of particular interest to Humanities or Social Sciences graduates.
Education
Education 01
Diverse contributions to educational research: conceptualisation, recognition, and supporting mechanisms
Supervisor
Dr Xin Xu
Theme
Education
Description
In this project, you will review, evaluate and develop resources and tools for recognising, crediting, and supporting diverse contributions to educational research, across different positions (including academic staff, research staff, research enabling staff, student researchers), career stages, career pathways, and sub-disciplines within educational research.
You will examine understandings of ‘contributorship’ in this field and review multidisciplinary crediting framework, test their adaptability and utility, and develop field-specific approaches for educational research.
Outcomes
You will join the team in the second phase of the project and will contribute to the review of frameworks, resources and guidelines, as well as to a multidisciplinary literature review and to the planning of interviews.
You will develop bibliographic and planning skills as well as an understanding of ethics and data management in the social sciences. Your work may be credited, with your agreement, in subsequent project outputs.
Entry requirements
You should have, or be studying, a degree in social sciences.
English
English 01
Editing neglected short Middle English poems
Supervisor
Dr Daniel Sawyer
Theme
English language and literature
Description
The Bodleian Library's collections include manuscript copies of short Middle English poems which have never been published, or were edited most recently more than a century ago. In this project, you will help transcribe, edit and contextualise some of these poems. Training in manuscript handling, in editing, and in reading later medieval English handwriting will be provided. Because the poems are short, they form convenient work packages in which to learn the full process from manuscript page to edition with brief commentary.
You will have the opportunity to gain experience working with special collections: an insight into research libraries as well as postgraduate work in English. At the project's end, you may have the opportunity to write a blog post, and will be guided through submitting the set of editions to an appropriate journal for publication, adding to scholarship's collective knowledge of the literature of the period.
Outcomes
The set of completed editions will be submitted to an appropriate journal, such as the Bodleian Library Record or Notes and Queries, giving you experience of journal processes. You may also have the opportunity to write a blog post about your experience for the English Faculty website.
Entry requirements
You should have experience studying English literature, or a joint-subject degree including English literature. Within that, experience of work with Middle English and/or experience of special collections evidence and/or an interest in poetics would be an advantage, but are not requirements.
Geography
Geography 01
Using AI to synthesise evidence for policy
Supervisor
Dr Pete Barbrook-Johnson
Theme
Artificial Intelligence (Geography)
Description
Through this project you will work on one of two related tasks:
- developing a prototype tool for using large language models (LLMs) to build causal maps of the causal assumptions in policy documents (ie using AI to summarise causal assumptions in multiple large policy documents); and
- interviewing policy advisors and analysts about the use of LLMs in the policy process (eg summarising scientific evidence, synthesising evidence).
Outcomes
Depending on which route you take for this project you will either:
- contribute to the main output of the software tool itself, which will then be used in Oxford research projects; or
- produce a policy brief or blog, but potentially also an academic paper.
Entry requirements
You should have either a computer science/programming background or a social science background depending on which area you are interested in.
Geography 02
Unveiling NLP’s role in climate change solutions
Supervisor
Dr Alok Singh
Theme
Climate Change (Geography)
Description
Your objective during this internship will be address the challenges of climate change with the help of NLP applications. You will assist in filling the gap in the existing literature and shed light on the unique challenges, opportunities and potential solutions for applying NLP techniques to address climate and sustainability risks for sustainable finance.
Your work will include information extraction from reports released by policymakers, using large language models for analysing legal documents and extracting relevant information to identify patterns, trends, and strategies and mapping and characterising the world’s most polluting assets using NLP and other AI technologies. In this internship, students will be at the forefront of applying NLP techniques to tackle critical climate change and sustainability issues. The proposed work will contribute to the development of innovative solutions and insights that have the potential to shape the future of climate research. This internship will provide an in-depth knowledge of NLP basics. A list of possible topics includes but is not limited to:
- text analysis and processing of large documents related to climate;
- dataset creation and curation using NLP techniques;
- using machine translation for translating policies or any climate-related information;
- climate or sustainability related chatbots/Question and Answering (QnA) models;
- use of multimodality for analysing the public’s sentiment toward recently introduced policy related to climate change (for example, by analysing tweets by Twitter/X users);
- external knowledge integration in assets localisation models, such as using satellite images and text to narrow the search space;
- multidisciplinary study that may involve climate risk, spatial finance, sustainable finance and NLP;
- assessment of physical or financial damage that occurred due to natural calamities by analysing data gathered from news articles and other government sources; and
- analysing climate change related legal documents, extracting relevant information or summarising them (climate litigation).
Outcomes
There are various outcomes of this project, which are listed below:
- you will have the opportunity to witness first-hand the practical applications of machine learning and natural language processing, gaining insights that extend beyond theoretical knowledge. This immersive experience will demonstrate how these cutting-edge technologies are poised to revolutionise their field in the near future;
- at the culmination of this internship, you will not only have the opportunity to contribute to a publishable research paper but will also gain hands-on experience in developing a deployable model; and
- you will gain experience in navigating and contributing to projects that require collaboration across various fields. This helps enhance individual skill sets but also cultivates a rich understanding of the importance of interdisciplinary teamwork, preparing students for success in the complex landscape of collaborative research and problem-solving.
Entry requirements
You should possess a basic knowledge of natural language process, machine learning, or another related field.
You will also need to have a passion for addressing climate change challenges, good programming skills (in Python, TensorFlow, PyTorch etc) and the ability to work independently and collaboratively in a research-orientated setting.
History
History 01
Mapping the rise of Christian kingship, 300-840
Supervisor
Dr Conor O'Brien
Theme
History
Description
You will work on gathering materials for the appendices of a forthcoming book on Christian kingship in the late antique and early medieval worlds. You will help identify and gather a collection of relevant maps that will supplement the text; there is also a possibility of working with mapping software to produce new maps if the researcher felt that was necessary. You will undertake research for other supplementary materials (lists of rulers and family trees) and collate the required information.
Outcomes
The supplementary materials that you work on may appear in the monograph, The Rise of Christian Kingship, to be published by Oxford University Press in 2025.
Entry requirements
You should have, or be studying, a history or any similar Humanities-type degree. Some knowledge of early medieval history would be an advantage, but is not necessary.
History 02
Middle Eastern migration in 19th and 20th-century North and South America
Supervisor
Professor John-Paul Ghobrial
Theme
History
Description
This project will give you the opportunity to join a large-scale collaborative research project that explores the lives and experiences of Middle Eastern migrants who travelled from the Ottoman Empire to the Americas in the late 19th and early 20th century.
At the intersection of migration studies, Middle Eastern history, and research into life-writing, the ‘Moving Stories’ project will give you an opportunity to engage with a wide array of English sources left behind by Christians and Muslims who left the Ottoman Empire to start new lives abroad. These sources range from published autobiographies, correspondence, and private diaries to local newspaper accounts, census records and oral histories.
As new identities were formed, the results of these generational shifts were far-reaching. Early battles with racism and sectarian prejudices sat alongside individual attempts at assimilation as Syrians, Turks and Arabs became, simply, Canadian, American, Brazilian and much more. More information about the Moving Stories project and the research team you would be joining can be found on the Moving Stories website.
Your research will combine a study of macro-process of migration and diaspora with close attention to individual stories of identity, mobility, and generational change. You will be given a set of case studies to focus on, for example, the sources related to a particular individual, community, or event, some of which are currently in the possession of private family archives that are collaborating with the project. Much of this work will be desk- and library-based in Oxford, but will also draw on digital sources available from public museums and repositories related to Arab-American history. There may be some scope for research to be undertaken in the National Archives and/or the British Library in London.
Under the supervision of Professor Ghobrial, you will become part of the larger Moving Stories research team. You will work closely with several post-doctoral researchers, within which you will have responsibility for specific segments of research that contribute to the project’s objectives. You will obtain a basic introduction into the methodological and ethical contexts relevant to working with family papers and will be asked to produce a preliminary descriptive catalogue of the sources related to the selected case studies, which will feed into a final piece of research published by the project.
Outcomes
You will have ownership of a few distinct research projects over the six-week period. This includes working with primary and secondary sources, as well as drawing up a descriptive catalogue of a set of archival sources. Depending on your ability and interests, this research will be disseminated in a research paper/article, a blog hosted on the Moving Stories site, and/or a website or short documentary focused on one individual family or community of migrants and their archive of papers (the storymap about one migrant family published by Linda Jacobs in November 2021 is an example of one such final product). There may be the possibility to contribute a number of focused encyclopaedia entries to the project's planned Sourcebook, which would result in you having your name on a publication at the end of the internship.
Your work will involve research into data and sources already identified by the project, and usually available for intensive study either online or in digital format. Secondary research will be conducted in the Bodleian, and you will benefit from the recent proliferation of digitised sources available in such repositories as the Smithsonian Museum (Faris and Yamna Naff Arab American Collection), the Khayrallah Centre for Lebanese Studies, and the Arab-American National Museum, to name a few. You will not need any prior knowledge of Middle Eastern history to complete this research.
Entry requirements
Relevant degree subjects include history, and desirable (but non-essential qualities) include experience of using newspaper databases and/or language abilities in languages other than English (for example, French, Spanish, and/or Arabic).
We hope to offer you training in the use of online sources, bibliographical software (eg Zotero), and newspaper databases (consistent with the routine training offered to Oxford undergraduates from the Bodleian).
History 03
What is the future for geothermal energy in the Caribbean?
Supervisor
Professor Amanda Power
Theme
Environmental humanities
Description
We urgently need new sources of 'green' energy and raw materials to transition to a low-carbon world. The hot fluids from volcanoes could deliver both via geothermal energy, and new sources of dissolved metals. Our new interdisciplinary programme on 'rethinking natural resources' is looking at the challenges and opportunities of geothermal energy on Montserrat, in the Eastern Caribbean.
We are looking for up to four students from different disciplines to explore some linked questions, including: Why and how has geothermal energy worked for Guadeloupe? (This island has the only operational plant in the Eastern Caribbean). What are the barriers to geothermal exploitation in the Caribbean? And what is the current status of plans for geothermal in the other islands of the Eastern Caribbean?
Individual projects could look at geological, social, economic, cultural or legal aspects of the question. Work will be desk- and library-based in Oxford, and could include analysis of archive materials, gathering, analysis and interpretation of data, and writing up a short report. Please see also project Earth Sciences 02.
Outcomes
You will get experience of working on a new, interdisciplinary project; of doing new research that will directly contribute to the work of the programme, and could help to shape new ideas about the future of geothermal energy and resources across the Eastern Caribbean.
Entry requirements
You should have a background in history, geography or environmental humanities. Please identify which areas from the project description are of particular interest when you apply.
Digital sociology (Oxford Internet Institute)
Internet Institute 01
Parental control app use in the UK
Supervisor
Professor Ekaterina Hertog
Theme
Digital sociology
Description
Mobile devices have become ubiquitous among children. At the same time, the use of parental control apps has spread quickly, deployed both to supervise online behaviour and to track children’s offline activities. These apps have potentially profound effects on modern parenting, children’s autonomy and development, and intra-family relations and trust.
There is very little research on how parents and children use these apps, how they perceive and negotiate their use within families. Ofcom has been collecting survey data on children’s and parents’ media use and attitudes since 2018. These surveys contain several relevant questions on the use of parental control apps within families. This project will focus on exploring and doing descriptive analysis of available Ofcom data focusing on parents’ and children’s perceptions of digital safety and the use of parental control apps in the UK.
Outcomes
At the end of this internship, you will have gained a deeper understanding of the ways families navigate the issues of digital safety as well as how different families make use of different kinds of parental control software. You will develop basic skills of survey data cleaning, exploration and descriptive analysis.
Entry requirements
You should have experience in a relevant social science discipline (eg economics, political science, sociology or anthropology) from your undergraduate degree. Some background in quantitative research methods (and use of STATA) is highly desirable but not essential.
Internet Institute 02
Media portrayal of parental control apps
Supervisor
Professor Ekaterina Hertog
Theme
Digital sociology
Description
This research project delves into the representation of parental control apps in newspapers and other popular media. As mobile devices become increasingly integral in children's lives, parents often resort to these apps for both online supervision and offline tracking. This trend raises significant questions about children's autonomy, development, and the trust dynamics within families.
The study aims to dissect how newspapers portray these apps, influencing parental perceptions and choices. By analysing newspapers, you will help us to understand the media narrative constructed around these apps. The findings will offer insights into the role of media in shaping public opinion towards digital monitoring and how it interacts with regulatory environment.
Outcomes
You will have the opportunity to learn to gather and analyse media discourse (and more broadly text-based discourse). You will also gain a deeper understanding of the ways media narratives shift over time and how these narratives interact with regulatory environment.
Entry requirements
Experience in a relevant social science discipline (eg economics, political science, sociology, anthropology) from your undergraduate degree is desirable, but not essential.
Law
Law 01
Furthering human rights under international law: advancing freedom of religion or belief
Supervisor
Professor Nazila Ghanea
Theme
International human rights law
Description
This project involves legal research to support the UN Special Rapporteur on freedom of religion or belief (a pro bono role the supervisor holds in addition to being a Professor at Oxford), in relation to two activities:
- research on case law and soft law of international human rights bodies (UN and regional human rights systems, eg European Court of Human Rights) on freedom of religion or belief and people on the move (migrants, refugees, and asylum seekers). This will inform the 2025 report to the UN Human Rights Council; and
- preparing country-specific research, analysing international human rights mechanisms, for upcoming country visits and briefs to other UN human rights bodies.
You will have the opportunity to learn about the functioning of UN and other international human rights mechanisms, carry out research, and shape the development of these standards. The research will be desk based and guidance will be provided.
Outcomes
The research briefs you will present will feed into three concrete products:
- the Special Rapporteur's thematic report to the UN Human Rights Council, which will be made public and presented to the Council in 2025;
- a Country Assessment (internal) and final report (external) for one of the Special Rapporteur's Country Visits; and
- submissions on behalf of the Special Rapporteur to other UN human rights mechanisms concerning freedom of religion or belief in particular countries.
Entry requirements
You should have good knowledge of and/or a strong interest in human rights. An interest in international human rights law and freedom of religion or belief would be beneficial but is not essential.
You should have strong research skills, be self-motivated and disciplined, and have strong communication skills.
Your undergraduate degree may include, but is not restricted to, the following: law, international relations, political science, sociology, anthropology, history and theology.
Linguistics, Philology and Phonetics
Linguistics 01
The prosody of corrective BUT-sentences in English and Spanish
Supervisor
Dr Jose Elias-Ulloa
Theme
Linguistics
Description
This project studies the syntax-prosody interface. In ‘Mary walks her dog [every morning]’, ‘every morning’ is a syntactic constituent and, normally we don’t put a pause before it. But when fronted, as in ‘Every morning, Mary walks her dog’, suddenly, there is a pause after ‘every morning’. Prosody refers to the rhythm and intonation of sentences and varies to reflect changes in the order of syntactic constituents. This project investigates how much prosody and syntax diverge in two types of Corrective BUT-Sentences:
- when negation occurs preceding the verb (eg Mary did not hug Paul, but John)
- when negation appears after the verb (eg Mary hugged not Paul but John).
We will do this in English and Spanish by identifying and analysing prosodic and acoustic cues at the boundary before BUT in both languages so we can uncover the principles behind the similarities and differences that we observe.
Outcomes
You will receive training in identifying different types of acoustic cues of segmental and prosodic phenomena that occur at the boundaries of syntactic constituents. This training will help you recognise the way spoken language is organized, both at the sentence level and in the way that we express meaning through intonation and rhythm. Specifically, you will learn how to use a system called ToBI (Tone and Break Indices) to annotate intonational patterns.
You will also learn to transcribe and acoustically analyse audio recordings of phrases that contain BUT-phrases. To assist with this, you will be using a program called Praat and a tool called Praat TextGrids, which are used for studying the sounds of speech. This will give you practical experience working with linguistic data in both English and Spanish, enabling you to compare these two languages.
At the end of your internship, you will be expected to write a final report based on your findings. This report will delve into the phonetic distinctions and similarities between English and Spanish based on data that you will analyse. Your supervisors will provide you with guidance, feedback, and suggestions for further research. This internship is an opportunity for your personal and professional development, offering you the chance to gain practical experience in linguistics.
Entry requirements
You should have taken an introductory course in linguistics, but a degree subject in linguistics is not a requirement for this project.
Ideally, you should be familiar with phonetic transcriptions. You do not need to be a native speaker of Spanish or English. But it would be beneficial if you have some basic understanding of Spanish language.
Linguistics 02
Coarticulatory processes in shipibo: a phonetic investigation of an Amazonian language
Supervisor
Dr Jose Elias-Ulloa
Theme
Linguistics
Description
Phonetics involves a comprehensive analysis of linguistic sounds: their articulation, perception, and acoustic characteristics. An integral component of sound articulation is coarticulation, where articulatory movements overlap continuously. For instance, take the English word 'pin.' When we pronounce the /p/ sound, a burst of air is released immediately after the lips part. This aspiration blends with the onset of the following vowel, causing partial devoicing. While producing the vowel, air simultaneously escapes through the nose in anticipation of the final nasal consonant, imparting a distinct nasal quality.
This project delves into the main coarticulatory processes of Shipibo, an Amazonian language spoken in South America. Our approach revolves around acoustic analyses. Utilising the program for phonetic analysis, Praat, we aim to examine spectrograms and identify the acoustic cues associated with various coarticulatory processes, such as vowel nasalisation and glottalisation as well as consonant velarisation, labialisation, and (de)voicing.
Outcomes
You will receive training on how to read spectrograms of speech audio recordings from Shipibo, a Pano language spoken in the Peruvian rainforest. You will learn how to identify diverse linguistic sounds that occur in this language. Some of these sounds are similar to English, while others are not. Furthermore, you will learn to transcribe those sounds using the conventions of the International Phonetic Alphabet (IPA).
To assist you in your task, you will use Praat, a program for acoustic analysis, and Praat TextGrids, a tool specifically designed for adding aligned phonetic transcriptions to audio recordings. This internship will provide you with practical experience in working with linguistic data from an Amazonian language that belongs to the Pano linguistic family. You will gain first-hand experience in analysing the intricate sound patterns of a non-European language.
You will be expected to write a final report focusing on a specific class of linguistic sounds or phonetic phenomena. You will choose this topic in consultation with your supervisor, who will also provide you with feedback and recommendations for further research. This internship offers an opportunity for personal and professional development providing a foundational experience in the phonetic documentation of the sound patterns of an Amazonian language.
Entry requirements
We are looking for enthusiastic students eager to explore the rich world of indigenous and endangered languages. If you have a knack for appreciating linguistic nuances and are familiar with phonetic transcriptions and the International Phonetic Alphabet (IPA), this internship is for you.
Music
Music 01
Building connections between early modern music manuscripts and prints
Supervisor
Dr Julia Craig-McFeely
Theme
Musicology
Description
The Digital Image Archive of Medieval Music (DIAMM) is the backbone of all research into early music and one of the longest-running digital humanities projects in the world. We make early music manuscripts available in the digital medium by photographing them and cataloguing their contents in a complex database. We teach skills in bibliography, managing a live online relational database, engaging with original manuscripts and understanding the environment in which they are studied; these are foundational skills for all research, not just in music.
The project team contributes new material to the research landscape on a daily basis, connecting and collaborating with major research initiatives and institutions around the world, and presenting research findings at conferences, symposia and workshops. Team members have access to a number of developing technologies that are not yet public, which means that there are also exceptional opportunities for those interested in development of web-delivery tools and the digital exploitation of music materials.
Outcomes
DIAMM is internationally known as a gold-standard musicology project. You will be given a solid grounding in bibliographic and research principles essential to postgraduate research in any subject. Our collaborations with a number of international partners and projects at the leading edge of digital musicology introduces interns to a rich landscape of opportunity and ideas.
At the end of the internship you will have identified a research project and will be well positioned to apply for postgraduate study in musicology or digital humanities.
Should you have an interest in programming, development of the online resource with our web-designer is an additional option.
Entry requirements
You should have a music degree or advanced skills in music and have strong computer skills. If you have an interest in medieval or early modern history and/or palaeography it would be advantageous. You will be a self-starter with the ability to work independently.
Theology
Theology 01
Understanding Gen Z's religious and spiritual role models
Supervisor
Dr Edward David
Theme
Theology and philosophy
Description
Increasingly, young people are disaffiliating from organised religion. The rise of the 'nones' (ie those with no religious affiliation) suggests that young people are looking elsewhere to find deep meaning and inspiration in their lives. Where are they looking? And, more specifically, who are they looking up to?
In this project, we will explore the types of religious and/or spiritual role models that young people have. Are their role models anything like the saints and sages of old? To identify these exemplars, we will use an insightful (and fun) qualitative research method - story completion - and we will write up our analysis in a publishable article manuscript.
Outcomes
At the end of the programme, you will have contributed to the data collection, qualitative analysis (including engagement with philosophical and theological literatures), as well as the writing of a co-authored article manuscript.
Together, we will shine light on who young people think are religiously and/or spiritually admirable and, by extension, to consider what this might mean for society. You will hit the ground running with data collection (eg using online channels to find a modest number of research participants). And, with the research team, you will help make sense of participants' responses as they come in. This sense-making will include diving into relevant literatures from theology, philosophy, and moral psychology.
In the final weeks of the programme, you may have the opportunity to contribute to the writing of the article manuscript from this small-scale, yet meaningful, qualitative study.
Entry requirements
A background in theology and religion, philosophy, and/or psychology is preferred. No experience in qualitative research methods is required just curiosity and an eagerness to learn. Familiarity with the latest, and most used, social media channels (eg Tik Tok, Instagram) will be useful.
Theology 02
Religious madness in the Age of the Asylum
Supervisor
Dr Edward David
Theme
Theology and history
Description
The aim of the project is to identify and transcribe first-hand narratives of “religious madness” (ie psychological and spiritual experiences that were considered disordered or insane) written in the eighteenth and nineteenth centuries. These data will be used in the longer term for multi-disciplinary research analysis.
Your primary task will be to help identify suitable narratives by conducting keyword searches in online databases (eg HathiTrust, Google Books, Worldcat, ECCO, archive.org), by reviewing existing bibliographies of madness narratives (eg Gail Hornstein’s bibliography), and by reviewing published collections of patient writings (eg the New York State Hospital’s journal of patient writings, The Opal, accessible online) as well as digitised archives of asylum documents (eg, Wellcome’s substantial online resource). You will also devote some time to transcribing narratives that have already been identified by the project supervisors.
Outcomes
You will develop skills and gain hands-on experience in various aspects of research in the humanities, including digital research methods, analysis of primary (historical) sources, data management, and collaboration with other scholars.
Entry requirements
You should have an interest in history, life-writing, and the health humanities. Experience using Excel would be useful but is not required.
Projects in Mathematical and Physical Sciences are offered by the following departments:
Chemistry
Chemistry 01
Inorganic chemistry for future manufacturing
Supervisor
Professor Simon Aldridge
Theme
Future manufacturing and inorganic chemistry
Description
You will learn skills associated with the synthesis of molecules, nanoparticles or solids, and gain critical experience handling inorganic compounds. Your work on these projects will focus on the synthesis of novel compounds with potential applications in fields such as catalysis, energy storage and chemical synthesis.
You will have the opportunity to select a project, in one of three areas associated with inorganic chemistry, prior to starting in the laboratory and will be assigned a supervisor with the relevant expertise.
Outcomes
You will have the opportunity to learn how to safely handle reactive compounds including materials that are air- and moisture-sensitive; analyse chemical compounds using state-of-the-art techniques (including, for example, nuclear magnetic resonance (NMR) spectroscopy, X-ray diffraction, mass-spectrometry, electrochemical methods); interpret experimental data; write scientific reports; and potentially present your research to an academic audience.
Entry requirements
You should have experience in chemistry, or a chemistry-related subject from your undergraduate study.
Chemistry 02
Computational modelling of network materials
Supervisor
Professor Mark Wilson
Theme
Future manufacturing and modelling
Description
An understanding of the properties of network materials is vital if their properties are to be fully controlled and exploited. The most well-known example, graphene, displays a number of key properties which may be controlled by changing topology (folding into fullerenes, nanotubes etc), introducing disorder (amorphisation - introducing non-six-membered rings), doping (for example with BN) or some combination of these.
Computational modelling, using relatively simple interaction models, allows the full range of these modifications to be explored in a systematic manner difficult to reproduce experimentally. By collaborating closely with experimental groups focused on these materials, models can act both to help interpret experimental results and to motivate further study.
In this project you will employ molecular dynamics methods to model these key materials and to explore their useful properties.
Outcomes
You will learn how to:
- model simple network materials;
- employ molecular dynamics methods to study static and dynamic properties;
- use languages such as Python to analyse data from the simulations; and
- use multi-node cluster machines to produce large datasets.
Entry requirements
You should have, or be studying towards, a degree in chemistry, physics, materials science, biochemistry or mathematics. Other subject areas may be considered.
Computer Science
Computer Science 01
Learning dynamical systems with physics aware machine learning
Supervisor
Professor Mark van der Wilk
Theme
Data science and AI and modelling
Description
Controlling the behaviour of physical systems is important in many applications in engineering (eg keeping a rocket on a prespecified trajectory, or allowing a humanoid robot to walk) and science (eg controlling plasma in a fusion reactor). A model that predicts the behaviour of a system can help to determine how to control it to get desirable behaviour.
Machine learning methods use data to learn to predict behaviour. While this is powerful, machine learning methods often need a lot of data, because they are not aware of properties of the system they are predicting, eg conservation of energy, which "physics inspired ML" seeks to remedy. This project aims to categorise different (Newtonian) dynamical systems, and create illustrations of how physics knowledge can speed up learning.
During this project, you will train and evaluate physics inspired ML models, and investigate characteristics that determine when physics knowledge helps most.
Outcomes
You will be training physics ML models on various dynamical systems, and making visualisations.
Our aspirational goal is for you to also categorise mathematical properties of dynamical systems which determines physics ML models' success.
Entry requirements
You should have, or be studying, a degree in engineering, physics, mathematics or computer science.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Computer Science 02
Logic and learning on graphs
Supervisor
Professor Michael Benedikt
Theme
Data science and AI
Description
In this project, you will look at learning in the presence of logical rules. How can we ensure that a learning algorithm is consistent with the rules? How can we use the rules to improve learning?
You will look at prediction problems where the input is a graph - 'graph learning'. You will focus on a machine-learning formalism called Graph Neural Networks (GNNs), which have become popular for a number of practical tasks, such as discovering links in a social network and drug discovery.
There are a number of GNN formalisms, and you will investigate what can and cannot be done with different types of Graph Neural Network, going through some tools - from combinatorics, probability, and logic - to analyse the networks and understand their behaviour. Effective learning with GNNs depends not only on the type of network, but on how the initial weights ('node features') are set. Therefore, we will also look at initialisation strategy, particularly randomised initialisation, and how it impacts the ability of a GNN to handle a task. Where you find that a particular kind of GNN cannot perform a task of interest, you will consider what needs to be added to the formalism to be able to perform the task.
Outcomes
You will develop skills in machine learning that should be useful later on. If the project goes well we hope to publish the results, and you may have the opportunity to contribute.
Entry requirements
You should have, or be studying, a computer sciences related degree and have standard software development skills at an undergraduate level. Familiarity with Python and machine learning libraries would be useful.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Computer Science 03
Application of machine learning methods to research problems in computational biology
Supervisor
Professor David Gavaghan
Theme
Biological systems and modelling
Description
Time series models are used to approximate the dynamic behaviour of dynamical systems and are ubiquitous across mathematical and computational biology. Embedded within the models are parameter values which govern model outputs and must be inferred from experimental data. Research in our group focuses on the development of machine learning algorithms for parameter inference in applications ranging from the modelling of pandemics, through the safety of new drugs, to the potential use of enzymes in developing biofuels.
Underpinning all of our research is our PINTS (Probabilistic Inference on Noisy Time Series) open-source software, which provides to the user an easy-to-use interface to the machine learning and optimisation algorithms that we have implemented within PINTS. Your work on the project will involve either further development of one of more algorithms within PINTS, or the application of machine learning techniques to one of the scientific problems that are of interest to our group.
Outcomes
During the project you will have the opportunity to develop an understanding of the use machine learning methods in parameterising biological models. You may have an opportunity to contribute to a research paper.
Entry requirements
You should have, or be studying, a quantitative degree (maths, physics, engineering, computer science) and interest in biological problems. Some programming experience would be helpful but is not essential.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Internet Institute 03
Privacy enhancing technologies: monitoring privacy protections and AI harms
Supervisor
Dr Luc Rocher
Theme
Data science and AI
Description
Scientific research relies on large-scale human traces, such as genomic sequencing data, electronic health records, and geolocation of millions of humans. Anonymising such data is often a difficult task, needed to widely share data with researchers, and reducing the threats to people’s privacy, including mass surveillance and identity theft.
The goal of this project is to study modern privacy-enhancing technologies (PETs) currently being developed, ranging from formal mathematical guarantees (differential privacy) to machine learning techniques (synthetic data generation). Social and health scientists are raising the alarm that these anonymisation techniques could compromise research findings, distorting statistical inferences and suppressing minorities.
Depending on your interest, you might participate in research testing the balance between the benefits these techniques offer and the potential harms that need to be mitigated. You could also work on building interactive web tools to convey the risks/benefits of using PETs to the public.
Outcomes
You will be involved in interdisciplinary research on privacy-enhancing technologies. You will learn to evaluate these techniques on benchmark and real-world data, and study how to model privacy guarantees and AI harms in practice. You will work on improving your programming and software engineering skills, and potentially web development depending on the direction of the project.
Entry requirements
You should have knowledge of basic machine learning, probability and linear algebra. A good understanding of statistics and fluency in Python/Julia would be beneficial.
Earth Sciences
Earth Sciences 01
Walking on the moon – analysing terrestrial analogues for lunar crust
Supervisor
Professor Richard Palin
Theme
Earth science and imaging
Description
You will examine samples of anorthosite collected from the Outer Hebrides, northwest UK.
Anorthosite is the main rock type that makes up the highlands on the Moon. Studying terrestrial examples can provide great insight into both how the Moon evolved soon after its formation, and for studying the geological processes that operated on the Precambrian Earth, potentially before the onset of plate tectonics.
Under the supervision of an experienced team of principal investigators you will combine petrography (eg optical microscopy) with quantitative mineral composition analyses (eg using SEM/EPMA) to determine evolutionary history of samples of anorthosite from the Isle of Harris. These results will be used to test competing hypotheses about the formation of anorthosite complexes on Earth and to compare their mode of formation to the lunar highlands.
Outcomes
You will write a short report documenting your findings, with assistance from the Principal Investigators (PIs), and will be mentored in how to prepare your scientific results for publication in high-profile, international journals. You will have the chance to meet with other members of the PIs research group and practise science communication through informal presentation of your work to your peers.
Entry requirements
You should have a passion for geology and planetary science.
Earth Sciences 02
What is the future for geothermal energy in the Caribbean?
Supervisor
Professor David Pyle
Theme
Net zero
Description
We urgently need new sources of 'green' energy and raw materials to transition to a low-carbon world. The hot fluids from volcanoes could deliver both via geothermal energy, and new sources of dissolved metals. Our new interdisciplinary programme on 'rethinking natural resources' is looking at the challenges and opportunities of geothermal energy on Montserrat, in the Eastern Caribbean.
We are looking for up to four students from different disciplines to explore some linked questions, including: Why and how has geothermal energy worked for Guadeloupe? (This island has the only operational plant in the Eastern Caribbean). What are the barriers to geothermal exploitation in the Caribbean? And what is the current status of plans for geothermal in the other islands of the Eastern Caribbean? Individual projects could look at geological, social, economic, cultural or legal aspects of the question. Work will be desk- and library-based in Oxford, and could include analysis of archive materials, gathering, analysis and interpretation of data, and writing up a short report. Please see also project History 03.
Outcomes
You will get experience of working on a new, interdisciplinary project and of undertaking research that will directly contribute to the work of the programme and could help to shape new ideas about the future of geothermal energy and resources across the Eastern Caribbean.
Entry requirements
You should have, or be studying, any physical or environmental science degree discipline (eg earth sciences, chemistry, engineering). Please identify which areas from the project description are of particular interest when you apply.
Engineering
Engineering 01
Understanding hydrogen-metal interactions to enable a green energy transition
Supervisor
Professor Emilio Martinez-Paneda
Theme
Net zero, analytical techniques
Description
Hydrogen is ubiquitous and its applications will drive the technology of a net-zero carbon society. However, it is also infamous for “embrittling” metallic materials, reducing – by orders of magnitude – their ductility (elongation), fracture toughness and fatigue crack growth resistance.
This so-called hydrogen embrittlement phenomenon is responsible for numerous hydrogen-assisted failures across the transport, defence, construction and energy sectors and, importantly, is considered one of the biggest impediments to the broader implementation of a hydrogen-based fuel economy, hindering the transition away from fossil fuels.
This project will use experimental techniques (electrochemical, mechanical) to understand how hydrogen degrades metals and develop new materials that can enable a green hydrogen energy infrastructure.
Outcomes
The research that you will be assisting with may potentially lead to scientific publications.
You will use electrochemical (permeation, desorption, galvanostatic/potentiostatic charging) and/or mechanical (tensile tests, fracture, fatigue) techniques, along with material characterisation to provide new understanding of the interaction of hydrogen with: 3D printed metals, an important area that remains largely unexplored, and new materials that hold promise in being suitable for hydrogen transport and storage.
The research that you will be assisting with may potentially lead to scientific publications.
Entry requirements
You should have, or be studying, a degree in engineering, materials or physics.
Engineering 02
A new generation of multi-physics models for material degradation and failure
Supervisor
Professor Emilio Martinez-Paneda
Theme
Net zero, modelling
Description
There is an opportunity now to develop a new generation of simulation-based models that can predict material degradation. This is made possible by ever-increasing computer power and the development of new finite element algorithms that can enable simulating concurrent (coupled) physical processes such as mechanical deformation, chemical reactions, diffusion of species and material fracture; so-called multi-physics modelling. These enriched continuum computer models bring quantitative predictive capabilities by resolving the underlying physics while delivering predictions at a scale relevant to engineering practice.
You will assist in developing and/or applying this new generation of multi-physics models to a variety of technologically relevant problems: from predicting the lifetime of wind turbines to enabling a breakthrough in battery technology.
Outcomes
You will develop and/or use computer codes to tackle key challenges of the energy transition, with the potential opportunity to contribute to a scientific journal publication.
Entry requirements
You should have, or be studying, a degree in engineering, materials, physics or mathematics.
Engineering 03
Partial water splitting towards green hydrogen
Supervisor
Professor James Kwan
Theme
Net zero, future manufacturing and analytical techniques
Description
Ultrasound activated bubbles (ie cavitation) can reach temperatures equivalent to the surface of the sun. At these temperatures, water is partially split into H and OH, which may recombine to form hydrogen. Through this project you will explore acoustic techniques to amplify the production of hydrogen by using the OH to convert low value chemicals to valuable ones.
Outcomes
You will produce green hydrogen from water and quantify the production of hydrogen (and other gases) using gas chromatography. You will also analyse chemicals in the liquid with liquid chromatography.
Entry requirements
You should have, or be studying, a degree in engineering, materials, chemistry or physics based subject. You should also possess familiarity with MatLab.
Engineering 04
Experiments in a hypersonic wind tunnel
Supervisor
Dr Tobias Hermann
Theme
Aerospace and analytical techniques
Description
A new hypersonic wind tunnel is currently being built at the Oxford Thermofluids Institute that will allow the creation of air flows up to 5000 m/s velocity and is equipped with high-speed pressure sensors that record the flow at different locations within the facility.
These flows can be used to experimentally simulate the conditions faced by hypersonic vehicles like the Space Shuttle or similar. You will help to implement a measurement chain for the pressure-sensors and to perform various experiments in the facility. The experiments will be used to characterise the performance and capability of the new wind tunnel.
Your work on the project will include hands-on experimental work as well as data analysis of the measurement results. There is also an option to test an instrumented model of NASA’s planned Mars sample return mission in the new facility.
Outcomes
Your assistance on this project will result in benchmark conditions for the new facility which will be used in future scientific research on hypersonic and high-speed flow. During this process you will learn about fundamentals of high-speed flow physics and how measurement technology is applied for such extreme conditions.
This is an opportunity for you to be part of a scientific group and to learn about adjacent projects that deal with high-speed flows, such as optical laser-diagnostics, plasma-flow heating etc. Results will be published at international conferences and in Journal articles.
Entry requirements
You should have enthusiasm and open-mindedness to learning new skills and getting involved with hands-on work. Whilst we are open to degrees outside the specific study area, as we will provide training for all necessary skills, you may find it beneficial if you have, or are studying, a degree in engineering, physics or maths.
Engineering 05
Business simulation models for AI-powered decision making
Supervisor
Dr Yangchen Pan
Theme
Business, data science and AI
Description
You will have the opportunity to assist us with the development of a versatile business simulation API designed for the rigorous testing of machine learning algorithms.
The API's primary objective is to provide a realistic and dynamic environment in which machine learning models can be trained and evaluated. The business simulation API will be equipped with customisable parameters, enabling researchers, data scientists, and developers to fine-tune and assess the performance of machine learning algorithms in diverse and challenging situations.
By offering a standardised and flexible platform, this API aims to accelerate the development and optimisation of machine learning solutions for real-world business problems, ultimately fostering innovation in the field of artificial intelligence.
Outcomes
You will build an interface that can be used like open AI environments.
Entry requirements
You should have strong programming skills and a background in business management.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Engineering 06
Efficient occupancy identification by deep learning from images
Supervisor
Professor Philip Torr
Theme
Data science, AI and imaging
Description
In this research project, we aim to develop an efficient and accurate method for occupancy identification using deep learning techniques applied to face images. The project's primary goal is to create a model capable of identifying occupants within a space by analysing facial features. By harnessing the power of deep neural networks, we intend to achieve a reasonable level of accuracy while optimising computational efficiency. This research has practical applications in various domains, including smart buildings, security and human-computer interaction.
Outcomes
You will build a face-occupancy database, and develop a deep learning model that can classify occupancy at a reasonable accuracy.
Entry requirements
You should be familiar with Python and a deep learning framework, eg Python/PyTorch.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Engineering 07
Experimental hypersonic aerodynamics
Supervisor
Dr Luke Doherty
Theme
Aerospace and analytical techniques
Description
This project will be undertaken in the Oxford High Speed Wind Tunnels located at the Oxford Thermofluids Institute. These facilities are used to study fluid flows at speeds in excess of 1 km/s which are experienced by re-entry vehicles, meteoroids and demising satellites.
You will contribute to ongoing experimental programs through analysis of data, design of experimental wind tunnels models, and the continued development and use of experimental techniques such as pressure sensitive paint, schlieren, infrared thermography and liquid crystal thermography. Your exact project tasks will be decided in discussion with your supervisor based on your interests and the needs of the wider research group.
Outcomes
You will be expected to learn the fundamentals of hypersonic flows, the working principles of the Oxford wind tunnels and the difficulties associated with ground testing at velocities in excess of 1 km/s.
You will learn about the experimental techniques and advanced instrumentation that are used to investigate these flows, including the use of high-speed cameras capable of recording at up to 1 million frames per second.
Entry requirements
A relevant degree (or equivalent training) in engineering, physics or maths would be beneficial, however there are no specific entry requirements as necessary training will be provided. Some familiarity with data analysis (using Matlab/Python etc) will be useful.
Engineering 08
CFD of unsteady flow interaction with tidal turbine blades (updated 2 February 2024)
Supervisor
Dr Amanda Smyth
Theme
Net zero and modelling
Description (updated 2 February 2024)
The UK is investing heavily in offshore renewable energy technologies in order to meet net-zero targets. Ocean energy is one of the most abundant natural renewable energy resources available in the UK, and devices to harness this energy include wind turbines, tidal turbines and wave energy converters.
One of the common challenges for these technologies is long-term robustness and survival in the harsh ocean conditions. A major source of damage and premature failure is unsteady aero-or hydrodynamic loading deriving from turbulence, waves or nonuniformity in the incoming flow. These are complex fluid dynamic phenomena, requiring high-order modelling tools to analyse, which is not achievable for industrial design procedures.
This project will investigate unsteady flow interaction with a model tidal turbine using high-order Computational Fluid Dynamics (CFD) simulations, with the goal of identifying the dominant flow features and physical processes. This will inform the development of low-order modelling tools for industry use. Results from an existing dataset from a full 3D turbine simulation will first be analysed, in order to identify the relevant flow features. Complementary 2D blade simulations in equivalent unsteady flow conditions will then be carried out using Reynolds-Averaged Navier-Stokes (RANS) CFD. The analysis will inform low-order model development applicable to industry-standard design software.
Outcomes (updated 2 February 2024)
You will receive training in RANS CFD simulations using OpenFOAM, an open-source CFD software. You will also use common post-processing tools such as Paraview, as well as write your own post-processing code in Matlab or Python. You will gain experience in fluid mechanics analysis methods and the dynamics of wind and tidal turbine power generation. If the project is successful, the results will be included in a larger body of work which will be collectively published in an academic journal or conference paper, in which you may have the opportunity to be included as a co-author.
Entry requirements
You should have taken a course in fluid mechanics, at least at an introductory level, as part of your degree. Some experience in using either Matlab or Python, or other language suitable for data processing, would be beneficial.
Engineering 09
Dataset condensation with neural regulariser learning for crossing architecture generalisation
Supervisor
Dr Boyan Gao
Theme
Health, data science and AI and modelling
Description
The project aims to improve the dataset condensation generalisation ability across different neural network architectures. Dataset condensation methods, based on meta-learning, learning-to-learn methods of synthesising significantly smaller training datasets compared with the true training sets to boost the neural networks’ training efficiency and reduce the dataset storage consumption, besides the cost of losing the dataset interpretability.
The existing data condensation methods are usually conducted on simple shallow neural networks (NN) due to the notorious computational cost of meta-learning, which increases dramatically with the size of NN. This makes the learned dataset biased towards those neural architectures. As a consequence, when deployed on novel unseen NN architectures, the learned NN performs poorly after being trained on these synthetic datasets. In this project, we will explore improving the synthetic dataset generalisation across different architectures, more specifically, learning a neural regulariser jointly with the dataset to regularise the meta-learning algorithm in the dataset learning stage and the neural network training in the dataset deployment stage.
You will help us to explore the algorithm design ranging from adding multiple architectures to the existing meta-learning protocol to embedding the topology of the neural network as the input for the perspective neural regulariser. Our algorithm will be developed on the standard dataset condensation benchmark with the public dataset and the given architecture of neural networks and ultimately applied to the medical dataset. This will be beneficial for both training medical neural and protecting the patient’s information by utilising the non-interpretable nature property, the privacy, of the learned dataset.
Outcomes
You will assist in generating a workshop-level paper and you may have the opportunity to contribute to a machine-learning conference paper.
Entry requirements
You should have basic machine learning and deep-learning-related knowledge and be comfortable with deep-learning frameworks such as Pytorch and Jax.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Engineering 10
Green Your code!
Supervisor
Professor Noa Zilberman
Theme
Net zero, Data science and AI
Description
Many programming courses estimate the efficiency of running code based on its execution complexity. But what about the carbon efficiency of the code?
Different access patterns to the CPU’s cache, main memory, network or storage can lead to significant differences in power consumption. Running the same code at different hours of the day can result in different carbon emissions. Still, there is no easy way to assess the carbon emissions of a program.
This project aims to develop an open-source calculator that compares the carbon efficiency of programs, allowing students to evaluate not only how computing-efficient their code is, but also how friendly it is to the environment. To achieve this goal, this project will leverage a combination of CPU tracing, power estimation and carbon intensity APIs.
This project covers topics in computer architecture, sustainability and software engineering.
Outcomes
As part of this project you will learn about computer architecture, efficient coding practices and sustainable computing. You will have the opportunity to gain experience in scripting, performance-aware programming, publishing code and artefacts, and - importantly - you will help reduce the carbon footprint of computing.
Entry requirements
You should have good programming skills, preferably in Python or C/C++. Basic knowledge in computer architecture is required. You should have, or be studying, a relevant degree in engineering, computer science, or a related subject.
Engineering 11
Adding networks to green applications
Supervisor
Professor Noa Zilberman
Theme
Net zero, Data science and AI
Description
Many initiatives, such as those by the Green Software Foundation, aim to develop more sustainable applications. These applications range in type, size, and discipline: from machine learning and AI, through cloud computing, to mobile games. While the design of these applications uses expert knowledge in green computing, it often misses a critical component: the Internet!
This project aims to add the cost of computer networks, such as the Internet, to carbon-aware applications. Starting from reproducing an existing green application, this project will study the scope and operation of the application, and amend it with carbon emissions due to data transmission across the network. It will further consider how the environmental cost of the application changes as a function of geographic distribution.
This project covers topics in computer networks, software engineering and sustainability.
Outcomes
As part of this project you will learn about computer networks, cloud computing, and sustainable computing. You will gain experience in scripting, reproducibility, publishing code and artefacts, and - importantly - you will help reduce the carbon footprint of the Internet.
Entry requirements
You should have programming skills, preferably in Python or C/C++. Basic knowledge in computer networks is required. You should have, or be studying, a relevant degree in engineering, computer science, or a related subject.
Engineering 12
Dynamic tissue engineering scaffolds
Supervisor
Professor Malavika Nair
Theme
Health, future manufacturing and technology development
Description
Degenerative diseases and injuries have not only seen a marked increase in mortalities but are also the major contributor to the rising disability in our aging populations. As a result, there has been significant interest in developing improved implantable medical devices, which aim to replace, support, and restore the function and mobility lost by diseased tissues.
The extra cellular matrix (ECM) of tissues is an excellent base material for therapeutic and regenerative biomedical devices, since they can mimic the biological, chemical and physical environment experienced by cells in healthy tissue. However, the biomedical devices currently fabricated from the ECM have limited tunability or dynamic control once implanted within the body. The aim of this project is to fabricate and test various dynamic tissue engineering scaffolds fabricated from biological polymers.
Outcomes
You will learn to fabricate and characterise tissue engineering scaffolds from ECM. While this project offers several possible avenues of investigation and professional development of various experimental techniques, there is some scope for you to define the the project in conjunction with the supervisor based on your research interests.
Entry requirements
You should have, or be studying, a degree in a science or engineering related discipline.
Engineering 13
Quantifying permeability changes in water-saturated sands during an earthquake
Supervisor
Professor Orestis Adamidis
Theme
Earth sciences and imaging
Description
During an earthquake, sand found below the water table can start behaving like a heavy liquid, in a potentially catastrophic phenomenon called “liquefaction”. Our ability to accurately model this phenomenon is hindered by the lack of reliable data on how the permeability of sand changes when it starts behaving like a liquid. This mechanism is important as it controls the magnitude of displacements structures suffer during an earthquake. During this project you will use a bespoke rig to examine liquefaction in two dimensions for a custom granular medium consisting of 3D-printed grains. Using a camera and digital image correlation you will track the movement of individual grains and assess how parameters that affect permeability – such as tortuosity – evolve on approach to liquefaction. Based on your findings you will propose a new function for permeability, likely as a function of the pressure of water between the grains.
Outcomes
You are expected to learn the fundamentals of fluid flow through porous media, soil dynamics, and digital image correlation techniques. The experimental results that will be the outcome of this project will be novel. As such, along with the proposed permeability function, they may result in a draft for a tier-one conference submission by the end of the internship.
Entry requirements
You should have, or be studying, a degree in engineering, computer science, physics or mathematics. Basic programming skills using Matlab/Python are desirable. All necessary training will be provided.
Engineering 14
Learning reward attribution for General-Sum games
Supervisor
Professor Jakob Foerster
Theme
Data science, AI and modelling
Description
Learning in general-sum settings (ie the iterated prisoner’s dilemma) is an open frontier for the AI community. Usually, in this setting we assume that the reward functions of individual agents are fixed and that as AI scientists we need to find methods that allow agents to perform well given these individual rewards. However, even if individual reward functions can be changed by the designer, this is not necessarily a trivial task: While summing up all of the rewards completely gets rid of the social-dilemma aspect and makes the problem fully cooperative, it does so at the cost of creating a very challenging credit assignment problem.
You will help us to investigate reward attribution and shaping methods for large-scale general-sum settings.
Outcomes
You will gain exposure to machine learning. In particular multi-agent learning and general-sum games.
Entry requirements
You should have strong quantitative skills, eg from a mathematics, computer science or physics background.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Engineering 15
Alternative methods of self-supervised learning of tractable generative models
Supervisor
Professor Philip Torr
Theme
Data science, AI and modelling
Description
Einsum Networks (EiNets), a class of generative models which allow for exact and efficient probabilistic inference, face training challenges like overfitting with Maximum Likelihood Estimation (MLE). This project explores Conditional Composite Log-Likelihood Estimation (CCLE) as a promising alternative, aiming to enhance EiNet performance on larger datasets.
We will compare various CCLE implementations against MLE, focusing on density estimation, inpainting, and regularisation effects. Additionally, we investigate a curriculum-based CCLE approach and assess CCLE's effectiveness in out-of-distribution detection. This research could significantly improve EiNets' applicability in real-world scenarios where rigorous reasoning under uncertainty is vital for example in safety-critical environments.
Outcomes
You will:
- gain a solid background in probabilistic ML and a scalable class of tractable generative models, EiNets;
- develop alternative and novel methods of training EiNets on a variety of datasets;
- evaluate methods against an existing set of CCLE training implementations and MLE training, gaining exposure to the evaluation of vision models; and
- write a final report on your findings of the research carried out comparing developed methods against standard MLE training and existing CCLE implementations.
Entry requirements
You should have, or be studying, a degree covering probability and statistics (including probabilistic inference such as marginalisation and conditioning and MLE in statistical models). You should have also a background in Python.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Materials
Materials 01
Extracting creep behaviour from cantilever bending and machine learning
Supervisor
Professor Angus Wilkinson
Theme
Future manufacturing and materials
Description
Tensile tests are often preferred over bend tests as the spatially uniform stress and strain states are easier to measure and interpret. However, the use of digital image correlation (DIC) to measure spatially varying strain fields across a whole sample are now commonplace. It is also relatively easy to use finite element methods to simulate the stress, strain and displacement fields that develop in a bend test assuming the materials properties are known. The inverse problem of extracting the materials properties from known loading and measured strain or displacement fields remains more difficult.
This project will seek machine learning or surrogate models that provide solutions to this inverse problem and open up high throughput routes for determining strain rate and time dependent (creep) properties from simple mechanical tests.
Outcomes
You will receive training in mechanical testing, digital image correlation, and finite element analysis, and then work independently with these methods to generate data. With help from group members, you will work to develop robust routes to solve the inverse problem and extract materials properties from the measurements and simulations.
You will present results to the Oxford Micromechanics Group (OMG). There will also be opportunities to see other techniques (eg SEM, EBSD, AFM and nanoindentation) in action with other members of the OMG.
Entry requirements
You should have a background in materials science, engineering, or physics. Basic knowledge of deformation is desirable.
Materials 02
Mechanical behaviour of sustainable high entropy alloys
Supervisor
Professor David Armstrong
Theme
Future manufacturing, analytical techniques
Description
High entropy alloys are a new class of material where no single element dominates. They have been shown to have a range of interesting mechanical properties. As there is no dominant element, there is potential to use as the basis of an alloying system for recycling. The composition may vary depending on the source material used, but the hope is that adequate mechanical properties can be achieved with a wide compositional window.
In this project, you will study the mechanical properties of a series of high entropy alloys based on the FeCrMnNi system with and without carbon additions. The compositions have been chosen to reflect typical elements found in steel recycling. You will study the mechanical properties through tensile testing and associated analysis methods. These samples have been manufactured by collaborators at Witts University in South Africa, and you will also collaborate virtually with researchers based there.
Outcomes
You will receive training in mechanical testing, digital image correlation, optical microscopy and image analysis and the use of Matlab, and then work independently with these methods.
With help from group members you will also use scanning electron microscopy to study your samples fracture surfaces. Data collection and will be at a level suitable for a journal publication. You will present results to the Oxford Micromechanics Group.
Entry requirements
You should have, or be studying, a degree in materials science, engineering or physics (solid state).
Mathematics
Maths 01
Parsimonious representations in deep neural networks
Supervisor
Professor Jared Tanner
Theme
Data science and AI
Description
Deep neural networks are the algorithm driving the advances in machine learning and artificial intelligence. Much of the research using these techniques involves algorithms with increasingly large numbers of trainable parameters, eg ChatGPT3 has 1.5 billion trained parameters. It is increasingly important to consider ways for these networks to achieve their task, but with substantially fewer parameters; such networks are often referred to as parsimonious. The simplest method for generating a parsimonious network is to remove entries which are believed to have little impact on the output of the network, and then retrain the network with just those entries active.
During this project you will explore methods for constructing parsimonious networks, including the aforementioned pruning as well as more sophisticated notions such as low-rank approximations and sketching.
Outcomes
You will learn what a deep neural network is and how to train it dispelling the mystery of modern AI, showing that underneath it is just mathematics. You will gain familiarity with Python using Google Colab as a simple working environment.
Entry requirements
You should be familiar with linear algebra and have some prior experience with computer programming (any language) or have undertaken a course in numerical algorithms.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Physics
Physics 01
Detecting trace gases in the atmosphere using satellite data
Supervisor
Dr Anu Dudhia
Theme
Climate and analytical techniques
Description
The project involves working within a group specialising in using satellite measurements of the Earth's infrared emission spectra to retrieve concentrations of a number of different atmospheric gases normally only present in small concentrations (SO2, NH3, C2H6, etc).
The project will involve writing Python code to analyse our data and compare these with other datasets, culminating in a written report and a final presentation to the group. The student will be based in an office with other summer project students, with whom they are expected to collaborate, and also participate in weekly group meetings.
Outcomes
You will produce a report summarising an investigation into the retrieval of a particular molecule of interest (to be decided nearer the time).
Entry requirements
You should preferably have, or be studying, a degree in physics and have Python programming experience.
Physics 02
What the fuzz? Automatically distinguishing stars from galaxies in the ATLAS sky survey
Supervisor
Dr Heloise Stevance
Theme
Space and imaging
Description
Modern sky surveys are excellent at spotting changes in the night sky, from supernova explosions in distant galaxies to outbursts of stars in our neighbourhood. Some of these stellar outbursts are hard to distinguish from supernova in the early days of the events just by tracking the evolution of the brightness - so humans have to take time to look at the image stamp to see if the source is near a galaxy or coincident with a star.
On this project, you will work on creating a simple classifier to automatically distinguish between source that look like stars and those that look like they belong to a galaxy.
Outcomes
The outcome of this project will be a classifier that will tag the new sources with the label "point" or "extended" (stars are point sources whereas galaxies are extended sources). In the first instance the labels will be used to prioritise which objects should be visually checked by researchers and considered for follow-up with other telescopes.
In the future if the classifier is found to be very accurate and complete for the supernova sample (such that we do not miss interesting objects) the classifications can be applied without requiring a human check.
Entry requirements
You should have, or be studying, a degree in mathematics, physics, engineering, or computer science. Python coding skills are an essential requirement. You do not require an astronomy degree.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Physics 03
The emission spectrum of axion strings
Supervisor
Professor Edward Hardy
Theme
Matter and modelling
Description
The QCD axion is among the best motivated candidates to comprise dark matter. However, experimental searches are hampered by the lack of a reliable prediction for the axion mass such that it accounts for the full cosmological abundance of dark matter.
During this project you will analyse a key ingredient in this prediction - the emission spectrum of axion strings (which form in the early universe). You will assist in adapting an existing C++ numerical simulation code to non-standard expansion histories, which will shed light on the emission spectrum.
Outcomes
The outcome of this project will be a new version of the simulation code that can evolve the axion string system in an arbitrary cosmological background, as well as data for the string emission spectrum, and an analysis of this that you will present in a report.
There may also be the opportunity to adapt your findings to be submitted as part of a research paper.
Entry requirements
You should have, or be studying, a degree in physics or theoretical physics including experience and enthusiasm for coding in C++, and some exposure to classical field theory and basic cosmology (FRW solutions).
Physics 04
Ultracold atom laboratory
Supervisor
Professor Robert Smith
Theme
Matter, analytical techniques and modelling
Description
The project will be based in our ultracold atom laboratory in which we cool erbium and potassium atoms down to nano-Kelvin temperatures to study of many-body quantum phenomena such as the recently realised super-solid state.
The details of the project you will be working on will depend on progress in the lab and will be finalised later but could involve design and construction of optical setups for cooling and trapping ultracold atoms, generation of custom magnetic fields for manipulation of atomic properties or numerical simulation of ultracold atom clouds.
Outcomes
You will gain experience in experimental techniques (eg in optics, electronics, data analysis) and gain insight into quantum gases. You will write up your work in a short report and if successful may have the opportunity to be a co-author on a publication.
Entry requirements
You should have, or be studying, a degree in physics.
Physics 05
Hardware development for a future electron-ion collider experiment
Supervisor
Dr Georg Viehhauser
Theme
Matter and technology development
Description
Oxford is involved in the construction of the ePIC experiment at the EIC at Brookhaven National Lab. We participate in the Silicon Vertex Tracker, with the specific task to deliver the outer barrel layers together with our UK collaborators.
Several aspects for this detector need to be developed and this project will give you the possibility to participate in one of them:
- development of advanced composite support structures: Verification and optimisation of mechanical performance on a low-intensity shaker table;
- development of gas cooling on local supports: Study and optimisation of cooling gas flow and heat transfer through ultra-low density foam; and
- testing low-drop-out powering schemes for serially powering the sensors, or electronics simulations of the same.
While most of these setups will be in an advanced state next summer, you will have the opportunity to work on advanced instrumentation, experiment DAQ, data analysis and input to the final design.
Outcomes
At the end of the project you will write a report documenting your work and findings. The results from this project may be used in the ePIC Technical Design Report to be due at the end of 2024.
Entry requirements
You should have, or be studying, a degree in physics.
Physics 06
Instrumentation development to search for dark matter with the DarkSide-20k Experiment
Supervisor
Professor Jocelyn Monroe
Theme
Matter and technology development
Description
This project will involve performance qualification of silicon photon sensors employed in the DarkSide-20k experiment. DarkSide-20k searches for dark matter particles, gravitationally bound to our galaxy, interacting in an ultra-sensitive terrestrial detector.
The signature of dark matter interactions in DarkSide-20k is light produced by the argon target. This light signal is detected by novel silicon photon detectors, composed of arrays of silicon photomultipliers (SiPMs).
You will learn to measure the photon detection performance of these cutting-edge silicon detectors in a cleanroom environment, employ calibration techniques and develop data analysis skills.
Outcomes
You will produce a brief report and oral presentation summarising the performance qualification results and the implications for dark matter search.
The project aims are for you to learn new skills in research at the low background frontier of particle physics, to contribute to the delivery of the silicon detector readout system that instruments part of the international DarkSide-20k experiment, currently under construction at the LNGS laboratory in Italy; and to gain experience with working as part of a research team.
Entry requirements
You should have, or be studying, a degree in physics, engineering or computer science and have laboratory experience.
Physics 07
Too close for comfort: is WTS-2 b spiralling into its host star?
Supervisor
Professor Jayne Birkby
Theme
Space and analytical techniques
Description
The TESS mission has been observing the entire night sky since 2018 to detect transiting planets ie those planets which pass in front of their host star along our line of sight. This means it can find both new planets, and deliver high quality follow-up light curves of known exoplanets. In this project, you will extract the light curve of the known planet WTS-2 b from the TESS full frame images using tools provided by the transiting exoplanet community. WTS-2 b is a hot Jupiter in a very short orbit that was discovered in 2014. Tidal decay theory suggests WTS-2 b is spiralling into its host star, where it can be ripped apart by tidal forces. The goal is to use the TESS light curve to search for either effects and design follow-up observations to confirm if there is missing physics in our prescription of tidal decay.
Outcomes
You will develop your skills in coding, and write a short report that can be used as the basis for an observing proposal for follow-up study of the exoplanet.
Entry requirements
You should have, or be studying, a degree in physics or another closely related topic. A basic familiarity with UNIX commands and Python would be beneficial.
Physics 08
Hunting for supersymmetric dark matter at the Large Hadron Collider
Supervisor
Professor Alan Barr
Theme
Matter and modelling
Description
Dark matter is one of the most prominent puzzles in modern particle physics. The mystery may be solved by supersymmetry, a theory that introduces a suite of new fundamental particles which physicists are searching for at the Large Hadron Collider (LHC), the world’s most powerful particle accelerator. In this project we will explore the hints of supersymmetry that could be present in LHC data.
There are several directions the project could take depending on your interests, including applying machine learning methods, statistical techniques and particle physics phenomenology.
Outcomes
You will produce a written report and, depending on your interests, gain experience using machine learning methods, statistical techniques, and/or particle physics phenomenology. The results of the project may motivate future LHC searches.
Entry requirements
You should have, or be studying, a degree in physics. Experience with Python, or a desire to learn, is essential. C++ experience may be useful but is not essential.
Physics 09
Simulating thunderstorms at very high resolution
Supervisor
Dr Edward Groot
Theme
Climate and modelling
Description
Convective thunderstorms produce heavy rainfall. We want to reliably predict the likelihood of these thunderstorms, to have early warning of associated hazardous flooding events. However, this is a difficult problem, because of the complex nature of thunderstorm drivers, and the limited predictability of the atmosphere.
In this project we will utilise several (idealised) very-high resolution computer simulations to relate environmental conditions to the probability distribution of thunderstorms and their intense precipitation. Thereby, a set of tiny initial perturbations to the flow (“ensemble technique”) will help us to account for the chaotic nature of the atmosphere and improve our estimates. This will allow us to improve insights into the predictability of thunderstorms, and the hazardous precipitation associated with them.
Outcomes
You will produce a report and/or presentation containing probabilistic models to describe rainfall intensity.
Entry requirements
You should have, or be studying, a degree in mathematics or physics, or potentially earth sciences. You should have a basic knowledge of fluid dynamics.
Physics 10
Source extraction for hydrogen detected radio galaxies
Supervisor
Dr Catherine Hale
Theme
Space and analytical techniques
Description
The MIGHTEE survey is one of the key survey projects of the MeerKAT telescope, which is observing radio galaxies both in the continuum (ie at one frequency) and using spectral information (at a range of frequencies to study Hydrogen, HI).
In this project, you will use a source finder (ProFound, Robotham+ 2018) which was designed for optical/infrared surveys but has been shown to be successful for radio continuum measurements (Hale+ 2019) to see how it extends to the spectral line data of MIGHTEE. You will use ProFound to extract sources across the MIGHTEE HI data and make comparisons to the results from previous source finders used on this data and demonstrate what the strengths/weaknesses of the source finder are when applied to HI data.
Outcomes
You will produce a report at the end of the project summarising the main areas of research. You will also be expected to perform a short presentation to colleagues in the research group to demonstrate the results of the project.
Entry requirements
Experience of Python and R is desirable as these will be used throughout the project.
Physics 11
Satellite measurements of volcanic clouds
Supervisor
Dr Isabelle Taylor
Theme
Climate and analytical techniques
Description
Emissions of gas and ash from volcanoes are hazardous to health and to aircraft. Additionally, they can have significant impacts on the environment and climate. Studying them is important for minimising the hazards they present and for better understanding their impacts. Satellite data offers the opportunity to study volcanoes across the globe, including in remote or difficult to access regions, and allows us to track emissions as they are transported away from the source.
This project will look at volcanic emissions using satellite instruments such as the Infrared Atmospheric Sounding Interferometer (IASI) with which we can learn about the composition of the plume and the amount/height of sulfur dioxide (SO2) and ash within the volcanic cloud.
Outcomes
You will produce a short report summarising the work done and results of the project.
Entry requirements
A background in physics, earth sciences, geography or similar subject area would be beneficial.
Some programming experience (eg Python) would be useful but not essential.
Physics 12
Extreme jets from black holes and neutron stars
Supervisor
Dr Alex Cooper
Theme
Space and modelling
Description
This project involves trying to better understand the underlying nature of powerful jets launched from black holes and neutron stars. You will use a simple code to try to recreate observations where some jets are directly observed, and others appear invisible to us for extended periods of time. We will compare how two types of astrophysical sources (black holes, and neutron stars) launch these jets, and apply the results to real-world observations of these extreme sources.
Outcomes
The aim of the project will be to produce a short report (2-3 pages) comparing two sets of jets and trying to explain our observations. You will also give a short presentation to astronomers in Oxford about the outcomes of the research.
Entry requirements
You should have, or be studying, a physics degree, with an interest in astrophysics. You must either have experience with, or be eager to learn Python programming language.
Physics 13
Mapping out the distribution of galaxies with machine learning
Supervisor
Dr Tariq Yasin
Theme
Space, data science and AI and imaging
Description
Photometric redshift estimation is a method used in astronomy to determine the distance of celestial objects, such as galaxies, from Earth. It relies on measuring the object's light across different wavelengths (colours) and identifying how much this light has been redshifted, or stretched, due to the expansion of the universe. This technique is especially valuable for studying distant objects where more precise "spectroscopic" measurements are not feasible, enabling astronomers to map the large-scale structure of the universe and understand its evolution over time.
In recent years the power of machine learning has been leveraged to improve the accuracy of the photometric redshifts. Machine learning algorithms are adept at processing and extracting meaningful patterns from vast modern datasets. In this context, we wish to investigate using a machine learning technique called Generalised Tomographic Maps (GTMs) (a probabilistic counterpart to Self-Organising Maps) to improve the accuracy of photometric redshifts.
The project will involve the development and testing of machine learning algorithms and benchmarking them against existing methods. By addressing the challenge of measuring cosmic distances more accurately, we aim to improve on a fundamental tool in modern astronomy.
Outcomes
The project will involve learning how to apply machine learning techniques to real world datasets. The outcome will be a short research note and/or a presentation to department members.
Entry requirements
You should have, or be studying, a mathematics, statistics, physics, astrophysics, computer science or another related subject that includes courses in linear algebra and statistics.
The project will require moderate computing skills, with coding in Python.
Physics 14
Automation for DNA nanotechnology
Supervisor
Dr Jon Bath
Theme
Future manufacturing and analytical techniques
Description
DNA nanotechnology allows construction of nanometre scale structures and devices; it is possible to design and assemble arbitrary shapes with feature sizes of just a few nanometres. Assembly is straight forward, approximately 200 short DNA strands are mixed and assembled by cooling from 90°C to room temperature.
In this project, we will explore automated assembly using a robotic platform (this will require simple Python programming and standard molecular biology techniques). Using a set of DNA pixels to pattern a DNA origami canvas and testing assembly using single-molecule fluorescence microscopy we will begin to explore the idea of a self-driving lab where experiments are executed and iterated in a closed loop that doesn't require user input.
Outcomes
The project aims to achieve a protocol for automated assembly of DNA nanostructure. You will gain a grounding in DNA nanotechnology, molecular biology skills and basic Python knowhow.
Entry requirements
You should have a background in engineering, physics or biochemistry.
Statistics
Statistics 01
Why do deep generative models not know what they don’t know?
Supervisor
Professor Chris Holmes
Theme
Data science and AI
Description
This project is motivated by an open problem in machine learning: We are interested in deep generative models (DGM), which are deep learning models that approximate a data distribution and thus allow us to generate samples from it, for example pictures of cats and dogs. At the same time, these models allow you to calculate how likely a given input is under the model (which, for example, knows about how cats look like).
DGMs showed remarkable successes in recent years in modelling high-dimensional data distributions (such as images, audio, text, …). However, they suffer from a fundamental flaw which was first presented in a paper by Nalisnick, Matsukawa, Gorur and Lakshminarayanan in 2018. When you train a (likelihood-based) DGM on a diverse dataset of cats and you show it an image of a cat and a dog, where the dog is from some rather “simple” dataset, they tell you the dog is more likely, even though they are supposed to model the distribution of cats! This tells us that we cannot use the likelihood of DGMs for anomaly detection, the task of determining whether an input is in- or out-of-distribution. But it also raises a more fundamental question of what DGMs such as ChatGPT actually learn.
In this project, you will help us to analyse this phenomenon by using a gradient of neural networks for out-of-distribution detection and to analyse specific research ideas we have already developed that fit with this greater project. Specifically, these ideas have the potential to provide better performance and significantly accelerate the algorithm.
This project is for you if you are interested in probabilistic deep learning and would rapidly like to theoretically understand state-of-the-art models used in research today and their implementation in code with a view to develop the project into a full research paper that prepares you for a career in academia.
Outcomes
You will implement and analyse specific research ideas within our larger project on out-of-distribution detection for generative models towards solving the open problem.
You may have the opportunity to contribute to an actual research project, with a view to publish the research (either standalone or as part of other projects).
Entry requirements
You should have a strong mathematical background and knowledge of machine learning and deep learning. The project is mainly empirical/experimental in nature, but theoretical understanding is key and a theoretical direction is possible to explore, too.
Prior experience in Python and specifically PyTorch will be useful but is not essential.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Statistics 02
Semi-implicit Bayesian experimental design
Supervisor
Dr Tom Rainforth
Theme
Data science and AI
Description
Designing optimal experiments is a central challenge across many fields such as drug design, nuclear fusion and market research to name a few. Bayesian experimental design (BED) offers a powerful mathematical framework to address this problem.
In this project you will explore a novel strategy for overcoming some of the computational challenges associated with BED. Specifically, you will use modern machine learning techniques to investigate a hybrid approach to BED that combines likelihood-based and likelihood-free (ie implicit) methods.
Outcomes
You will start the project by learning about Bayesian experimental design (BED), specifically about recent frameworks like Deep Adaptive Design and its implicit-likelihood extension (iDAD). You will learn about the computational challenges in BED and how making some distributional assumptions can help alleviate them. You will empirically evaluate the new semi-implicit BED approach against previous machine learning-based methods and you will write a final report on your findings.
Entry requirements
You should have, or be studying, a degree in computer science, statistics or a another closely-related subject. You should have experience of machine learning and programming with Python. Experience with PyTorch would also be beneficial.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Statistics 03
An information-theoretic approach to measuring dataset difficulty
Supervisor
Dr Tom Rainforth
Theme
Data science and AI
Description
In machine learning, we typically use datasets to evaluate models, and there is much less emphasis on using models to understand and evaluate datasets. Focusing on computer vision tasks, the aim of this project is to explore dataset difficulty through the lens of V-usable information. This can provide insights into the specific attributes that make a dataset or particular examples difficult for a given model. Such insights can impact many aspects of AI development, such as creating more effective benchmark datasets or refining data curation strategies in active learning.
Outcomes
You will start the project by learning about the fundamental concepts of information theory (Shannon entropy, Shannon information), and the recently developed framework of V-usable information that generalises it. You will then apply V-information to pretrained vision transformer (ViT) models to quantify the difficulty of different computer vision datasets. You will write a final report on your findings.
Entry requirements
You should have, or be studying, a degree in computer science, statistics, information engineering or a another closely-related subject.
You should have experience of machine learning and programming with Python. Experience with PyTorch would also be beneficial.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Statistics 04
Parallelising ImmuneBuilder
Supervisor
Professor Charlotte Deane
Theme
Health, data science and AI
Description
ImmuneBuilder is a deep learning architecture that predicts the protein structures of three types of immune protein (antibodies, T-cell receptors, and nanobodies) with a predictive accuracy on par with Alphafold. ImmuneBuilder makes predictions an order of magnitude faster than Alphafold, enabling high-throughput structural modelling for immune proteins as part of drug discovery pipelines.
Despite the improved run-time of ImmuneBuilder, the architecture remains computationally heavy, and takes a substantial amount of time to train. The goal of this project is to optimise the training and inference of ImmuneBuilder by another order of magnitude, which can be achieved by optimising the pipeline of data through the model and by introducing batched data.
This project is suitable for candidates seeking to improve their practical knowledge of complex deep learning architectures and learn about protein structures.
Outcomes
The research outcomes of this project are:
- a more efficient implementation of ImmuneBuilder;
- a characterisation of the improved model’s performance by training the model and inferring the structure of different immune proteins with it;
- developing your skills in technically complex deep learning architectures and good practices in Python based software engineering; and
- increased knowledge of state of the art tools and methods in deep learning and structural protein informatics.
Entry requirements
You should have, or be studying, a degree in computer science, bioinformatics, or another relevant field.
This project is suitable for those seeking to improve their practical knowledge of complex deep learning architectures and learn about protein structures.
Prerequisites are experience with the deep learning framework PyTorch, an understanding of deep learning and an affinity for solving technically intricate problems, an interest in or knowledge of protein or immuno- informatics, equivariant graph networks and understanding of attention mechanisms such as those implemented in the Transformer.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Statistics 05
Machine learning and AI for SARS-CoV-2 main protease inhibitor discovery
Supervisor
Professor Garrett Morris
Theme
Health, data science and AI
Description
You will learn about how to apply the latest machine learning and AI technologies to help discover new inhibitors of a key drug target in SARS-CoV-2, the virus that causes COVID-19. By training models on binding data and 3D atomic structures of inhibitors of SARS CoV-2 main protease, you will advance our understanding of how to block viral maturation and how to develop new drugs to treat COVID-19.
Outcomes
You will explore data from the COVID Moonshot project, as well as explore a variety of classical ML models and more advanced methods such as Graph Neural Networks, Atomic Environment Vector-based models, and molecular Transformers such as Uni-Mol.
Entry requirements
You should have a background in mathematics, statistics, computer science, biochemistry, chemistry or physics.
Experience of Python is essential.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Projects in Life and Medical Sciences are offered by the following departments:
Biology
Biology 01
Reinforcement learning in epidemiology
Supervisor
Professor Moritz Kraemer
Theme
Health, data science and modelling
Description
Public health decision makers are faced with complex decisions during disease outbreaks. Decisions involve careful understanding of the uncertainties related to their data and models. Reinforcement learning with human feedback provides an opportunity to create a feedback loop between data, models, and expert knowledge. Whether these novel techniques arrive at better outcomes, however, remains to be seen.
In this project you are expected to develop a conceptual framework for the integration of reinforcement learning in the modelling of complex infectious diseases driven by climate change.
Outcomes
The project will potentially result in a peer reviewed publication.
Entry requirements
You should have, or be studying, a degree in statistics, mathematics, computer science or engineering.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Biology 02
Measuring the fitness cost of antibiotic resistance
Supervisor
Professor Craig MacLean
Theme
Health and microbiology
Description
Antibiotic resistance will be one the biggest challenges that humanity will face in the 21st century. Resistant infections cause over 1 million deaths per year, and this is predicted to increase to 10 million deaths by 2050. The goal of research in the MacLean lab is to understand the ecological and evolutionary processes that drive the rise and fall of resistance.
The evolution of antibiotic resistance in pathogenic bacteria is often accompanied by fitness costs, and these costs are thought to be one of the main mechanisms that prevent the spread of resistance. Unfortunately, bacteria can evolve compensatory adaptations that offset these costs and stabilise resistance. In this project you will use lab experiments to measure the fitness of a time series of antibiotic resistant bacteria, testing for fitness costs and compensatory evolution.
Outcomes
This project will provide you with skills in microbiology, evolutionary biology and antibiotic resistance and you will be provided with training in these areas. You will assist in generating a publication-quality data set on the evolutionary drivers of antibiotic resistance.
Entry requirements
Students from any academic discipline can apply, but undergraduate study in biosciences or life sciences would be particularly relevant.
Biology 03
Engineering plant responses to environmental cues
Supervisor
Professor Francesco Licausi
Theme
Food security, biochemistry and molecular biology
Description
With this project, you will assist in designing, testing, and optimising molecular devices, such as genes, non-coding RNAs, or proteins. These devices, when expressed in plant cells, play a crucial role in regulating adaptive responses to environmental cues. Our laboratory focuses particularly on the perception of gaseous molecules like oxygen, carbon dioxide, and ethylene.
Initially, these molecular devices are transiently tested in plant cells using leaf or protoplast transient transformation methods. You will help assess gene and protein expression in response to stimuli through enzymatic assays (such as luciferase assay), gene expression analysis (using semi-quantitative real-time PCR), and protein accumulation and modification studies (via western blot techniques).
Depending on the project stage, you may have the opportunity to test the ability of transgenic plants to adapt to various environmental challenges.
Outcomes
You will play a role in our strategy to enhance plants' ability to adapt to environmental challenges using synthetic biology techniques. Throughout this process, you will acquire essential laboratory skills in handling nucleic acids and proteins. Additionally, you will be responsible for caring for the plants used in our experiments.
Engaging in both formal and informal discussions with team members will train your critical thinking and analytical skills, specifically in the fields of plant genetics, molecular biology and biochemistry. Should the results merit publication, there may be the opportunity for them to be included in peer-reviewed articles.
Entry requirements
You should have a basic knowledge of molecular biology and biochemistry.
Biology 04
Using AI to quantify biodiversity
Supervisor
Professor Rob Salguero-Gomez
Theme
Biodiversity, data science and AI
Description
Classical methods to quantify biodiversity are time-consuming, error-prone, and expensive. In this project, you will have the opportunity to combine fieldwork in Wytham Woods with computational analyses via AI and machine learning algorithms to help quantify and identify species diversity.
The setting takes place in an experimental design where we are manipulating the environmental conditions of a biodiverse grassland under different treatments of fertilisation and mechanical removal (two of the most pressing threats to biodiversity in the UK). As such, you will also have a chance to explore different future scenarios via ecological forecasting techniques.
Outcomes
You will work on a computational pipeline to identify species and quantify grassland biodiversity.
Entry requirements
You should have an interest in computing analysis.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Biology 05
Comparative genomics and evolution of resistance genes in legumes
Supervisor
Dr Tin Hang (Henry) Hung
Theme
Food security and bioinformatics
Description
Legumes (the Fabaceae family) are the third largest family among angiosperms, which have significant ecological and economic values because of their nitrogen-fixing ability and dietary protein production. However, they are also susceptible to a wide suite of pathogens. Availability of newer genomic assemblies, especially from non-model species, helps understand the evolution of disease resistance in this important family.
Outcomes
The overarching goal of the project is to characterise the R genes in legume genomes and to study their evolutionary history. By the end of the project, you will have:
- curated a database from literature and database searches;
- acquired a suite of bioinformatic skills, including protein motif prediction, comparative genomics, and phylogenetics; and
- produced a short report on the project results.
You may have the opportunity to contribute to a publication and this project could provide useful skills for your future studies, careers, and personal development.
Entry requirements
These skills would be helpful to the project but are not essential as training will be provided: proficiency in UNIX and programming languages (especially Python and R) and experience in cluster computing.
You should have a genuine interest in the subject, a willingness to learn, and a commitment to the project.
Biology 06
Parental care and the evolution of cooperation in insects
Supervisor
Dr Rosa Bonifacii
Theme
Biological systems and phylogenetics
Description
This project aims to investigate the relationship between parental care and the evolution of cooperative group formation in insects. Extended parental care is commonly considered to be an important pre-condition for the evolution of sociality. However, the exact mechanism driving this relationship remains unclear.
Using phylogenetic analyses, we will explore the mechanisms connecting these behaviours, focusing on predictions from the “Assured Fitness Returns” Hypothesis. The research will involve conducting comprehensive literature searches to building upon a large pre-existing dataset on insect sociality by adding details of parental care behaviours. You will then design and implement phylogenetic comparative analyses to explore hypothesised relationships between specific aspects of parental care and the evolution of sociality.
Outcomes
You will be a part of ongoing work aiming to understand the fundamental principles underlying the evolution of social behaviour in the natural world.
You will gain hands-on experience in phylogenetic comparative techniques including both the process of data collection and the application of statistical techniques. It is likely that the data collected as part of this project will contribute towards a publication resulting from this work.
Entry requirements
You should have an interest or knowledge of behavioural ecology and/or principles of social evolution.
Biology 07
Reducing the environmental impacts of Merton College's food
Supervisor
Professor EJ Milner-Gulland
Theme
Food security and sustainability
Description
Reducing the environmental impacts of the food we eat is critical for tackling climate change and biodiversity loss. Working with Merton College's Head Chef and the college's sustainability committee, you will explore ways to reduce the environmental footprint of the college's food.
This will include considering diet swaps (such as swapping red meat in the college's menus for other proteins) as well as options for tackling food waste and changing sourcing patterns. Importantly you will also collect and analyse data on the financial implications of potential changes in food provision; you could also look at the relationship between nutrition and environmental impact.
This project will be part of a broader research programme which is supporting food retailers, canteens and individuals to understand, monitor and reduce their food's environmental impacts, and within this broad remit you will be able to choose areas of particular interest to you.
Outcomes
The project will help Merton College to meet its goal of reaching Net Zero carbon emissions and Biodiversity Net Gain by 2035.
You will gain experience in co-designing a research programme with people who will be implementing the changes you recommend. You will also form part of a team of people testing a new tool (called FoodMetric) that builds on a foundation of research carried out at Oxford to quantify the environmental impacts of food.
Entry requirements
You should have, or be studying, a degree in any discipline or background.
Biology 08
How environmentally sustainable is soft fruit?
Supervisor
Dr Joseph Poore
Theme
Food security and sustainability
Description
The global soft fruit trade provides an important case study to understand the environmental trade-offs which may arise between air miles, pesticide use, energy demand, and greenhouse gas emissions. Studies have found that importing soft fruit from the EU might be more environmentally beneficial in many cases than producing them domestically. What does this mean for farmers and policymakers? Studies have also found the residues of up to 20 different pesticides on fruits like strawberries, while other studies have found the impacts of air freighted strawberries are higher than pork and chicken – what does this mean for the consumers and the environment?
The purpose of this internship is to upload data to the HESTIA platform, particularly focusing on soft fruits produced in greenhouses. You will work with data in simple and highly complex greenhouse systems. Crops will include tomatoes and berries, grown in the UK and top producing countries abroad.
Your work would support our recent collaboration with The Waste and Resources Action Plan (WRAP), funded by the Department for Environment, Food & Rural Affairs (DEFRA). You will be helping to systematically generate harmonised and validated data on the average GHG emissions for a range of crops, such as tomatoes and berries, produced in greenhouses. The day-to-day work would involve sourcing and reading studies, extracting inventory data, and adding these to Excel files in appropriate formats. These data would then be uploaded to the HESTIA platform.
Outcomes
You will create a new assessment of the environmental impacts of a range of soft fruits in different countries under different production systems.
You will gain knowledge of: crop production systems, quantifying agricultural sustainability (in particular using Life Cycle Assessment), and conducting literature reviews. You will gain particular skills in: Excel, Git, and also knowledge of working with basic JSON schemas.
You do not need prior skills in these areas and will have opportunities to learn on the job. A basic understanding of these skills would however offer you a faster start. You will also have the opportunity to work with a range of researchers from our team, including environmental scientists and software developers.
Entry requirements
You should have, or be studying, a degree in any discipline or background.
Biology 09
Understanding leaf anatomy to improve photosynthesis
Supervisor
Dr Tina Schreier
Theme
Food security and biological systems
Description
Photosynthesis is the vital process by which plants utilise light energy to convert atmospheric CO2 into biomass. Some plants have evolved a more efficient form of photosynthesis known as C4 photosynthesis. A key feature of C4 photosynthesis is the polar positioning of chloroplasts in specialised leaf cells. However, the mechanism behind the formation of this unique chloroplast positioning remains unknown. We have generated knock-out mutants in a candidate gene (CHLOROPLAST UNUSUAL POSITIONING 1, CHUP1) that may be involved in this process in the C4 plant Gynandropsis gynandra using CRISPR-Cas9 gene editing.
The objective of this project is to study chloroplast positioning in the mutant plant using various microscopy techniques. We will then explore how chloroplast positioning affects photosynthetic efficiency.
Outcomes
You will be offered training and experience in a range of different techniques. You will determine whether transgenic CRISPR-Cas9 G. gynandra plants are successfully edited in the CHUP1 gene using Sanger sequencing. Additionally, you will perform the phenotyping of these mutants using light, fluorescence and electron microscopy to examine leaf ultrastructure and chloroplast positioning.
Furthermore, you will evaluate the impact of this mutation on photosynthesis by measuring photosynthesis levels and conducting chlorophyll fluorescence measurements at various light levels. Importantly, you will learn how to design experiments, formulate hypotheses, evaluate results – all crucial skills for a scientist.
Entry requirements
You should have a background in biology or a related subject.
Clinical Medicine
Clinical Medicine 01
Decoding the epitranscriptome in cancer
Supervisor
Professor Chunxiao Song
Theme
Cancer biology
Description
Our genome is far from static; it hosts a dynamic array of chemical modifications that play pivotal roles in development and disease progression. Our group aims to unravel the intricate world of DNA and RNA modifications, collectively known as epigenetic and epitranscriptomic modifications, within the context of human health with a particular focus on cancer. Our ultimate goal is to translate this knowledge into diagnostic and therapeutic innovations that directly benefit patients. In this project, you will contribute to the development of cutting-edge technologies designed to identify important epitranscriptomic modifications and the application of these tools to shed light on their involvement in tumour development.
Outcomes
You will learn how to conduct and interpret biochemical experiments that will contribute to the groups' research.
Entry requirements
You should have, or be studying, a degree in chemistry, biology or related science area.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Clinical Medicine 02
A genetic approach to understand light-sensing proteins in the skin
Supervisor
Professor Richard White
Theme
Cellular and translational biology
Description
Throughout evolution, animals have developed complex sensory systems that allow them to respond to environmental cues. One of these is the ability to sense light, which plays a crucial role in timing the internal biological clock and directly influences growth, survival, and behaviour. While most light sensing occurs through the eyes, photosensor proteins have also been identified in the skin and other tissues. Our lab is interested in understanding the function of these extraocular photosensors using the zebrafish as a model organism.
This project will focus on photosensor proteins found in melanocytes, the specialised skin cells that produce melanin pigment in response to sunlight and UV radiation. You will help us to use the gene editing technique CRISPR/Cas9 to generate zebrafish embryos with mutations in important melanocyte pigmentation genes. These embryos will then be raised in a variety of lighting conditions and their responses measured through gene expression, metabolic, and phenotypic assays.
Outcomes
This project will result in the production and characterisation of zebrafish larvae with mutations in melanocyte genes, and advance our understanding of how melanocytes respond to visible light.
You will gain experience with a widely used technique for genome editing, other common molecular biology techniques (RNA extraction, qRT-PCR), and designing experiments using zebrafish.
Entry requirements
There are no specific entry requirements though a background or interest in biological sciences and genetics would be helpful. Experiments will involve working with zebrafish larvae, but all necessary skills will be taught.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Clinical Medicine 03
How do errors in DNA folding cause disease?
Supervisor
Dr Robert Beagrie
Theme
Cellular and translational biology
Description
Cornelia de Lange Syndrome (CdLS) is a genetic disease that affects multiple organs including the brain, heart, limbs and gut. It is usually caused by mutations in NIPBL, a gene that controls the way DNA folds in the cell nucleus. In order to study the role of DNA folding in the development of different tissue systems, we use mice in which one copy of the Nipbl gene is knocked out.
We have generated high resolution 3D imaging datasets from mouse embryos carrying Nipbl mutations and control, wildtype embryos. In this project, you will be trained in how to analyse these datasets and to identify the developmental issues that commonly affect CdLS patients (eg malrotation of the gut). This would allow us to pinpoint the affected organ systems and design future experiments to understand why those tissues are specifically affected by errors in DNA folding.
Outcomes
You will be trained in computational software used to analyse 3D imaging data, in how to measure the size and shape of various different organs of interest, and in which statistical tests to apply to identify significant differences between mutant and wild-type embryos. You will also have the opportunity to observe and potentially contribute to other molecular biology experiments going on in the lab during your stay.
At the end of the project you will present your findings back to the group in an internal meeting. If any aspect of your analysis is included in a future publication, you may be included as a named co-author on that paper.
Entry requirements
Students from any degree discipline may apply; all required training will be provided.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Clinical Medicine 04
Automating splice-switching oligonucleotide design with deep learning
Supervisor
Professor Nicola Whiffin
Theme
Cell biology and computational modelling
Description
The use of splice-switching antisense oligonucleotides (ASOs) in genetic therapy shows promise by influencing splicing patterns. Currently, designing these ASOs involves time-consuming tiling assays due to the unpredictable impact on splicing patterns and the presence of latent splice sites. Deep learning tools, such as SpliceAI, have demonstrated the ability to predict sequence determinants of mRNA splicing. This project aims to use SpliceAI to predict the splice-switching capabilities of theoretical ASOs.
You will assist in automating the design process, reducing the need for laborious experimental assays and enabling cost-effective development of ASOs. The proposed in silico ASO tiling assay will be validated and refined using existing experimental data, contributing to a more efficient and reliable approach for optimising splice-switching oligonucleotide design.
Outcomes
You will gain experience in genomic analysis, programming and data science best practices as well as developing a proof-of-concept for deep-learning based SSO design which may inform further studies.
Entry requirements
You should have a background in science, technology, engineering or mathematics (STEM). You need to be able to program in either R or Python.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Clinical Medicine 05
Cellular plasticity and cancer
Supervisor
Professor Xin Lu
Theme
Cancer biology
Description
An ongoing project in our laboratory is the identification of molecular mechanisms that control cellular plasticity and suppress tumour growth. Cellular plasticity, the ability of cells to change their characteristics in response to various signals, underlies the initiation of cancer, metastasis and resistance to therapy. We are particularly interested in how external signals and the microenvironment, including infection and inflammation, can produce changes in gene expression that alter cell behaviour and cell fate. Our work has a particular focus on regulators of cellular plasticity in upper gastrointestinal cancers, including oesophageal and gastric cancer.
Projects in our group use a combination of molecular biology and cell biology techniques, including single-cell technologies, and various microscopy techniques. You will work with us on close collaborations with clinical research, and analyse clinical samples using state-of-the-art genomic, transcriptomic, epigenomic, proteomic and immunological analysis technologies.
Outcomes
You will become more confident in a number of molecular and cell biology techniques used in the Lu lab, in addition to various microscopy techniques and experimental design and analysis. The work that you undertake will contribute to our ongoing research into the role of plasticity in cancer initiation and progression.
Entry requirements
You should have, or be studying, a degree in biological and life sciences, molecular and cell biology, cancer biology, medicine or another related subject.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Clinical Medicine 06
Are repetitive sequences the ‘dark matter’ of the human genome?
Supervisor
Dr Parinaz Mehdipour
Theme
Cellular and translational biology
Description
Recent research suggests that reversible modifications on the DNA and RNA molecules within cells play a crucial role in cancer development, revealing differences between cancer and normal cells. However, current methods targeting these modifications in cancer, lack high effectiveness.
Recent studies have demonstrated that drugs targeting these modifications can reactivate specific repetitive sequences in the human genome. These drugs stimulate the patient's immune system to respond as if the cancer cells were infected by a virus. This response is triggered by the drugs' activation of repeat sequences in the human genome, producing RNA strands resembling viral RNA. Our research aims to investigate whether the regulator of these repetitive sequences in cancer cells can provide valuable insights into the effectiveness of cancer treatment.
In this project, you will learn to culture, maintain and treat cancer cells, isolate RNA and quantify gene expression at both mRNA and protein levels.
Outcomes
You will join our team to contribute to an ongoing research project within our group, focusing on identifying regulators for repetitive sequences and exploring how these sequences can be exploited for cancer treatment. Additionally, you will receive training in basic laboratory skills, which includes searching for your scientific topic of interest and reading scientific papers.
Entry requirements
You should preferably have basic knowledge in biology, however, this is not essential.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Clinical Medicine 07
Investigating epigenetic regulation to enhance cancer vaccine efficacy
Supervisor
Dr Carol Leung
Theme
Cancer biology
Description
Cancer vaccines have the potential to induce anti-tumour specific immune responses to reject tumours. Melanoma antigen gene (MAGE)-type antigens are frequently chosen as the target antigens in cancer vaccine development, as their expression profile is restricted to cancerous cells and suppressed in normal cells.
We have shown a heterologous prime-boost vaccination strategy targeting MAGE-type antigens promotes tumour T-cell infiltration and improves checkpoint blockade therapy. However, the vaccine efficacy can be compromised by low/no levels of MAGE-type antigen expression in tumours. MAGE-type genes are regulated through epigenetic regulations. Using epigenetic modulators to actively induce and increase MAGE-type antigen expression could be a potential approach to enhance vaccine efficacy. Our preliminary results show that epigenetic modulators can induced and increase of MAGE expression, both in vitro and in vivo.
You will help us to investigate the potential synergy between viral vector-based vaccines and epigenetic inhibitors as a combination therapy strategy.
Outcomes
You will learn how to culture cells in the lab and to process tissues in order to obtain single-cell suspension, and stain immune cells for flow cytometry analysis.
Entry requirements
You should have an interest in biology and medical science research.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Clinical Medicine 08
Understanding health policies through text analysis
Supervisor
Dr Attakrit Leckcivilize
Theme
Health data science
Description
Text analysis using machine learning techniques have been used in various fields such as political science and economics. These text analysis tools can help to group the US Presidential inaugural addresses based on measures of ‘similarity’ or extract key themes and topics from the speeches, while official statements from central banks can be used to explore policy makers’ sentiment/outlook of the economy. Despite the usage of these tools with, eg patients’ records and feedbacks, they have not been employed to study health policy extensively.
This project aims to use text analysis tools to explore, extract and visualise key information from key speeches of the health policy makers in the UK over the last 10-20 years. We expect the results from this project to be a proof of concept and feed into a further exploration on this topic across countries and international organisations.
Outcomes
We expect the findings from the project to be published as research article(s) in a peer-reviewed journal. If possible, it will also be used to support our team's future application for external funding to explore international health policy agenda.
Entry requirements
You should have a good understanding of coding/programming and a willingness to learn new machine learning techniques. An interest in health care issues and policies would be beneficial.
Clinical Neuroscience
Clinical Neurosciences 01
Investigating the role of TRPC3 in spinocerebellar ataxia
Supervisor
Professor Esther Becker
Theme
Cellular and translational biology
Description
The spinocerebellar ataxias (SCAs) are a complex group of inherited neurodegenerative disorders that are characterised by the dysfunction of the cerebellum. Patients affected by SCA have difficulty with walking and balance, fine motor skills, speech and swallowing, as well as eye movements. SCAs are debilitating diseases and can lead to an early death. No effective treatments currently exist for patients and there is thus an urgent need to develop new therapeutic strategies.
This project will focus on the role of a calcium channel called TRPC3 in the cerebellum and on how aberrant function of this channel causes disease. We are particularly interested in identifying ways to modulate TRPC3 function that could lead to novel treatments for SCA patients. The project will involve cell culture and transfection of mammalian cells, functional assays and immunofluorescence microscopy.
Outcomes
You will gain experience in experimental design, cell culture techniques and other laboratory work as well as data analysis. Overall, the project will help us to understand how abnormal TRPC3 function causes disease and how this this ion channel could be targeted therapeutically. You will present your work at a lab meeting and also produce a short report.
Entry requirements
You should have a background in life sciences, biology, biochemistry, pharmacology or a related area. There are no specific entry requirements but an interest in neuroscience and/or in ion channels would be desirable.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Medicine
Medicine 01
AI deep learning for clinical research
Supervisor
Dr Qiang Zhang
Theme
AI in clinical practice
Description
AI deep learning is transforming the world in many aspects, but its real-world clinical applications have been hindered by the knowledge gap between machine learning scientists and clinicians. We actively address this by developing AI algorithms next to clinical doctors at an interdisciplinary clinical research unit.
You will have the opportunity to work with a cross-disciplinary team to gain experience in developing deep-learning solutions for unmet clinical needs. This may include observing clinical workflow, data pre-processing, neural network design, data analysis and method validation.
Outcomes
You will gain domain knowledge of both deep learning and cardiovascular imaging, skills in medical data processing, neural network design in Python, and valuable experience in developing AI algorithms in clinical research settings.
Entry requirements
You should have a background in computer science or engineering, and experience in machine learning and coding in Python.
Funding information
This internship may be funded by Google DeepMind. The benefits of a Google DeepMind-funded placement are the same as those for UNIQ+ but you will also be expected to attend additional community building activities including a training session run by Google DeepMind prior to UNIQ+, and another after it has concluded, likely in June and September. Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about UNIQ+ Google DeepMind placements.
Medicine 02
Developing gene therapy for rare diseases
Supervisor
Professor Deborah Gill
Theme
Cellular and translational biology
Description
It has recently become possible to edit a person's DNA with the hope of treating multiple diseases. This is an important area of investigation for many genetic diseases that are otherwise difficult to treat. We are testing approaches for diseases caused by dominant mutations that result in a gain-of-function, such as alpha-1-antitrypsin deficiency. We are using both in vivo and in vitro (cell culture) models to develop gene editing approaches. In this project, you will focus on mammalian cell culture and the sequencing of DNA from a selection of gene editing models.
Outcomes
You will have the opportunity to work alongside a postgraduate student helping to develop a deeper understanding of gene delivery and gene editing models, and use the results to improve therapeutic approaches. You will have the opportunity to culture human and murine cells, design sequence assays and measure gene editing efficiency in a variety of models. Skills that you will learn include molecular biology techniques, mammalian cell culture, gene delivery to human cells, experimental planning and data analysis.
Entry requirements
You should have a life sciences or medical sciences background.
Medicine 03
Why does "bad" blood flow cause cardiovascular disease?
Supervisor
Professor Ellie Tzima
Theme
Cellular and translational biology
Description
Our arteries are exposed to various types of blood flow depending on their shape. When blood flow is turbulent, endothelial cells that line arteries become inflamed and activated, resulting in chronic inflammation and development of plaques. These plaques can obstruct blood flow to the heart or brain and cause heart attacks or strokes. The mechanisms by which endothelial cells sense and respond to turbulent blood flow are a mystery.
Work from our group has identified specialised receptors expressed on the surface of cells whose function is to detect blood flow and send signals that ultimately result in disease. One of these receptors is called Plexin D1. We now aim to understand in greater detail the mechanism by which Plexin D1 senses blood flow and how it signals to other cells to form a plaque.
You will be involved in some combination of tissue culture cell-based experiments, molecular biology, and advanced microscopy techniques. The project has the potential of being tailored to suit your research interests and techniques you want to specialise in.
Outcomes
You will have the opportunity to learn several lab techniques which are highly transferable. These include mammalian cell culture, application of shear stress to cells in culture, western blotting, qPCR, immunofluorescent staining of cells and tissue and confocal imaging.
You will attend weekly lab meetings and present your work in front of the entire group to hone your presentation skills. You will also receive mentorship and guidance if interested in pursuing a PhD or a future career in research.
Entry requirements
You should have a background or research interest in biochemistry, cell biology, vascular biology or physiology.
Orthopaedics, Rheumatology and Musculoskeletal Sciences
NDORMS 01
Regulatory network of neutrophil development in health and disease
Supervisor
Professor Irina Udalova
Theme
Immunology
Description
Neutrophils exert anti-microbial activity through several mechanisms including release of cytotoxic products, reactive oxygen species, neutrophil extracellular traps, and pore-forming molecules.
The presence of immature neutrophil subsets with abnormal functions are consistently associated with inflammation-driven pathologies, including sepsis, vasculitis, and severe COVID-19. The molecular control of pathogenic neutrophil responses is largely unknown.
You will help to generate the regulatory blueprint of neutrophil states during development and in a signal-driven microenvironment. The mapping of the regulatory blueprint will be achieved using multi-scale computational analysis of publicly available and in-house genomics data. Network analysis and machine learning models will be implemented to identify and inform key transcriptional regulators and neutrophil states crucial to development in homeostasis and inflammation.
Outcomes
The outcome of this study is expected to progress fundamental biology of neutrophils, increase our understanding of neutrophil activated subsets in disease and aid the development of new targets for therapeutic interventions in inflammatory disorders.
Entry requirements
You should have, or be studying, a degree in bioinformatics, statistics or mathematics and an interest in biological sciences. It would be beneficial if you possessed basic knowledge of programming languages such as R, Python, and shell.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
NDORMS 02
Benchmark study of data cohort instantiation and characterisation methods for health data research
Supervisor
Dr Martí Català Sabaté
Theme
Health data science
Description
The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) created by the OHDSI community has gained widespread adoption as a framework for analysing healthcare data. To facilitate the utilisation of OMOP CDM for research as part of the Darwin EU project. The developer team of the Darwin EU project have created in-house packages to create data cohorts and perform data characterisations. However, it is imperative to assess the performance of these in-house solutions against the standard methods recommended by the OHDSI community.
The primary objective of this project is to conduct a comprehensive benchmark study comparing different methods for data cohort creation and performing data characterisations in the context of OMOP CDM. You will be helping to evaluate in-house-built packages by the Darwin EU developer team against the OHDSI standard approaches, with a focus on assessing their performance, speed and accuracy.
Outcomes
The outcome of this study is expected to be an interactive web app for the developers to visualise the performance of different methods compared and help them to understand the limitations of their packages.
You should expect to gain skills and work experience in R programming (for data manipulation, analysis, and visualisation), managing and sharing code with GitHub and in handling sensitive and complex health datasets.
You may also have the opportunity to be a contributor on an in-house open-source package.
Entry requirements
You should have knowledge of a programming language as well as a good understanding of statistics. Ideally you should also have previous experience in contributing and sharing code with GitHub and R programming.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
NDORMS 03
Improving how medical research is planned, done, and shared
Supervisor
Dr Paula Dhiman
Theme
Health data science
Description
At the UK EQUATOR Centre within the Centre for Statistics in Medicine, we are a team of statisticians and meta-researchers involved with many research studies looking at how medical research is done, how well it is reported in the published literature, and how it can be done better. We conduct many of our own methodological research studies, including systematic reviews and surveys. One example is that we are evaluating the use of artificial intelligence in cancer. We also work with other researchers and clinicians in designing and conducting their studies.
You will work with us on our ongoing research studies to gain experience and insight into all steps of doing and evaluating medical research studies; from working with us to formulate research questions, design medical research studies, learn and improve how to code using specialised statistical analysis software, analyse data and help disseminate research through contributing to writing a research article. Which studies you are involved with will depend on your interests, but are most likely to include systematic reviews, methodological reviews or prediction modelling. Previous internships have included students working on writing up their research projects for publications and presenting their work at departmental seminars.
Outcomes
You should receive specialised training on research methods and analysis, and may have the opportunity for co-authorship on the project publication.
Entry requirements
The project is most suitable for students with a medical or statistical background, but you are welcome to apply from any subject background.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
NDORMS 04
Do human macrophages undergo senescence?
Supervisor
Dr Roel De Maeyer
Theme
Cellular and translational biology
Description
As we get older, many changes occur within our cells and tissues. One such change is senescence, the process of irreversible cell cycle arrest. When cells become senescent, they also often become inflammatory. This is known to happen in structural cells such as fibroblasts, but we know much less of what happens to a set of important immune cells called macrophages. These cells are the sentinels of our tissues, protecting us from infection and injury, but as we age macrophages become more inflammatory which could contribute to tissue dysfunction. We are interested in determining if macrophages can undergo senescence and if this contributes to their inflamed phenotype in older people.
You will learn primary human cell culture, western blotting and immunofluorescence during this project.
Outcomes
You will help us to understand if macrophages can undergo senescence and if this underlies an age-related inflammatory phenotype. Knowing this could help identify new targets for drug discovery research with a view to improving health span.
Entry requirements
You should have, or be studying, a degree in biochemistry, immunology, life sciences, biomedical sciences or a related subject.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
NDORMS 05
Investigating an off switch for chronic inflammation
Supervisor
Dr Anja Schwenzer
Theme
Immunology
Description
We are working on a new treatment strategy for people with the chronic inflammatory disease rheumatoid arthritis (RA). Inflammation is a naturally, and pro-actively, self-limiting response. It is switched on to fight infection and repair tissue injury and switched off once these dangers are overcome. People with RA cannot effectively turn off inflammation in the joint and this causes progressive destruction of tissue. We developed a peptide that can reinstate these off switches by preventing inflammation and restoring tissue homeostasis. However, the precise mode of action of the peptide is not known. Global transcriptomic changes were profiled by RNAseq in immune cells cultured under inflammatory conditions and treated with the peptide.
You will help us with the bioinformatic analysis of the transcriptomic dataset and to validate candidate gene expression and associated signalling pathways by qRT-PCR, ELISA and Western Blotting.
Outcomes
By contributing to an ongoing research project in the Midwood group, you will gain valuable experience in standard bioinformatic, cellular and molecular biology methods (pathway enrichment; immune cell isolation, differentiation, and activation; RNA and protein isolation; quantification of mRNA and protein expression) as well as learning how to plan your own experiments and analyse experimental data. At the end of the project, you may present your findings at an internal group meeting and your data might be published as part of an article.
Entry requirements
You should have, or be studying, a degree in immunology, biochemistry, molecular biology, medicine, life sciences or another related subject.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
NDORMS 06
A comparison of Cox proportional hazards model and flexible parametric models in randomised controlled trials
Supervisor
Dr Sofia Massa
Theme
Health data science
Description
The project will be dedicated to the study of survival data arising from randomised controlled trials. In survival data, the outcome of interest is usually a binary outcome (presence versus absence of a disease or death versus survival) accompanied by a follow-up time.
The Cox proportional model is typically used to model a survival outcome accounting also for covariates of interest. More recently, flexible parametric models have also been proposed for this type of data. You will examine and compare the modelling of survival data using Cox proportional hazard models and flexible parametric models using Stata (or R). Advantages and disadvantages of both models under different settings will be investigated and reported.
Outcomes
You will gain a deeper understanding of survival data, Cox proportional hazards model and flexible parametric models. You will contribute a technical report based on the findings.
Entry requirements
You should have knowledge of statistics, medical statistics or data science.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
NDORMS 07
Assessing the use of hierarchical mixed effect models and their assumptions in randomised controlled trials
Supervisor
Dr Sofia Massa
Theme
Health data science
Description
Hierarchical mixed effect models allow modelling of complex structured data, for example data arising from randomised controlled trials where outcome data is collected from participants belonging to different hospitals or centers and at different time points.
The aim of the project is to assess the use and assumptions of these models and to study how violation of assumptions impacts the parameter estimation of the models when they are fitted to randomised controlled trials data. There already exists a wide literature looking at assumptions for these types of models but their findings have not been extensively explored with data arising from randomised controlled trials.
Outcomes
You will have the opportunity to gain a deeper understanding of hierarchical mixed models, their assumptions and applications in randomised controlled trial data. You will contribute a technical report based on the findings.
Entry requirements
You should have knowledge of statistics, medical statistics or data science.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
NDORMS 08
Explore neutrophil maturation and effector functions with 4D microscopy
Supervisor
Professor Irina Udalova
Theme
Immunology
Description
This project uses cutting-edge microscopy technology to dissect the link between neutrophil maturation and their functions. Neutrophils are key innate immune cells that play a crucial role in responding to microbial threats and preserving tissue health. However, if not properly regulated, they can also exacerbate inflammation and cause tissue damage. Recent research has highlighted the significant heterogeneity and adaptability of neutrophils, including the role of immature neutrophil subsets in diseases.
Our project focuses on understanding how various transcription factors influence neutrophil maturation and their effector functions. We'll employ CRISPR-Cas9 to genetically modify neutrophil cell lines, targeting specific transcription factors. We'll evaluate the maturation stage and functional aspects (eg production of reactive oxygen species, NETs, and phagocytosis) in mutant cell lines using 4D lattice lightsheet microscopy. This research aims to identify potential targets for reprogramming neutrophils in disease.
Outcomes
You will help characterise the effect on neutrophil maturation and effector functions of the generated genetically modified neutrophil cell lines using innovative 4D microscopy.
Entry requirements
You should have a background in molecular and cellular biology, immunology, biology or biochemistry. Some coding knowledge would be useful but is not essential.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Oncology
Oncology 01
Characterisation of novel hypoxia induced targets in breast cancer
Supervisor
Professor Ester Hammond
Theme
Cancer biology
Description
Conditions of low oxygen or hypoxia are seen in most breast cancers. One of the cellular responses to hypoxia is changes in gene expression, and, often, the induction of specific target genes. The more hypoxic a tumour is, the worse the patient prognosis. Therefore, we are investigating novel hypoxia target in breast cancer which could be linked to the most aggressive cancers.
You will learn how to culture breast cancer cell lines and carry out western blotting for proteins of interest.
Outcomes
You will receive training in basic cell biology and may have the opportunity to contribute to a research article.
Entry requirements
You should have, or be studying, an undergraduate degree in a biological sciences related subject.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Paediatrics
Paediatrics 01
Studying cardiac development using mouse models of congenital heart disease
Supervisor
Dr Nancy Stathopoulou
Theme
Developmental biology
Description
Congenital heart disease (CHD) occurs during pregnancy and is the most frequent birth defect, affecting around 1 in 100 births. Defects can affect different parts of the heart, with severity ranging from no or few symptoms to very severe, potentially leading to neonatal lethality. Regulation of gene expression during the early stages of cardiovascular development is essential for the correct formation and function of the heart.
We are using mouse models of CHD and embryonic stem cells to study the biological mechanisms that control normal cardiovascular development. We aim to understand how these processes are disrupted in disease, focusing particularly on the role of epigenetic modifiers known as chromatin remodellers.
In this project you will explore the gene expression changes in mouse embryos with CHD, using staining methods such as whole mount in situ HCR (hybridisation chain reaction), RNAscope, immunofluorescence, followed by confocal imaging and image analysis. We also work with mouse embryonic stem cells, so you may also have the opportunity to gain experience in stem cell culture, differentiation methods, and molecular biology techniques (such as real-time qPCR, western blot, etc).
Outcomes
You will have the opportunity to learn a variety of basic laboratory skills applicable in many laboratory contexts in addition to project specific skills, analytical techniques, project management and critical thinking. You will also receive mentorship and guidance if you are interested in pursuing a career in biomedical research.
Entry requirements
You should have a biology or scientific background. However, technical training will be provided.
Pathology
Pathology 01
How cells build complicated protein machines
Supervisor
Professor Jordan Raff
Theme
Cell biology and computational modelling
Description
Centrosomes are complicated protein machines that play an important part in organising eukaryotic cells, and centrosome dysfunction has been linked to a plethora of human diseases. Almost all cells are born with a single centrosome that grows and divides; when the cell divides, each daughter inherits one centrosome and the cycle starts again.
During the project you will use sophisticated microscopes to make videos of living fly embryos expressing fluorescently-tagged versions of the key proteins that drive centrosome assembly. You will then analyse this footage using computational methods to track how the centrosomes grow and divide through multiple rounds of division. The quantitative measurements you make will allow us to mathematically describe how centrosome growth and division are regulated during embryo development, providing important insight into how these processes go wrong in disease.
Outcomes
You will learn how to use sophisticated microscopes and to computationally extract quantitative data from large image datasets. You will also learn some techniques in molecular biology and genetics.
Entry requirements
You should have some experience of biology, chemistry and/or computing. However, there are no specific degree requirements.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Pathology 02
Biophysical analysis of T-cell receptor interaction with antigens
Supervisor
Professor Omer Dushek
Theme
Immunology
Description
T cells are important white blood cells that orchestrate immune responses. They can be activated when they recognise the molecular signatures (antigens) of infections. T cells do this using their antigen receptors (TCRs). When this recognition is accurate, it can be helpful leading to the elimination of viruses and bacteria (foreign antigens) but when inaccurate, it can lead to autoimmunity (self-antigens) or allergy (innocuous antigens).
The ability of T cells to discriminate between different antigens depends on the binding affinity/kinetics between the TCR and the antigen. However, the difference in affinity/kinetics between the TCR and foreign vs self-antigens is unknown. Here, TCRs and antigen will be purified and their interaction studied in a biophysical technique known as surface plasmon resonance.
You will analyse data by fitting mathematical models in order to extract the binding affinity and kinetics for different interactions. The objective will be to determine the difference in affinity between foreign and self-antigens.
Outcomes
You will gain valuable experience in protein production, a popular biophysical assay for binding, and mathematical modelling. The research findings may be included in a future research study.
Entry requirements
You should have, or be studying, a degree in molecular biology, biochemistry, biophysics, or biological sciences.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Pathology 03
Molecular membrane biology: mechanisms and biomedical significance
Supervisor
Professor Matthew Freeman
Theme
Cellular and translational biology
Description
The Freeman group researches the molecular mechanism and biological functions of membrane proteins in cells, and their role in health and disease. Membrane proteins make up about 30% of the human proteome and over half of all drug targets. Our particular focus is the rhomboid-like superfamily, which we discovered, and which are increasingly recognised as being multi-functional regulators of a wide range of membrane proteins.
We employ a wide range of biochemical, cell biological and imaging techniques, as well as relying on genetics to ensure that our results are biologically relevant.
You will be working with current postgraduate students and postdocs to participate in their research projects, and will develop hands-on experience of molecular biology research techniques, completing a discrete project.
Outcomes
You will be involved with a substantial element of an ongoing research project, allowing you to gain practical and project planning experience.
Entry requirements
You should have a background in biological or physical sciences or medicine.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Pathology 04
Mechanisms of protein degradation
Supervisor
Professor Pedro Carvalho
Theme
Cellular and translational biology
Description
Accumulation of misfolded proteins and aberrant protein aggregates are hallmarks of a wide range of pathologies such as neurodegenerative diseases and cancer. Under normal conditions, these potentially toxic protein species are kept at low levels due to a variety of quality control mechanisms that detect and selectively promote their degradation. Our lab investigates these protein quality control processes with a particular focus on ER-associated degradation (ERAD), that looks after membrane and secreted proteins. The ERAD pathway is evolutionarily conserved and in mammals, targets thousands of proteins influencing a wide range of cellular processes, from lipid homeostasis and stress responses to cell signalling and communication.
We investigate the mechanisms of ERAD using multidisciplinary approaches both in human and yeast cells. We are using CRISPR-based genome-wide genetic screens and light microscopy experiments to identify and characterize molecular components involved in the degradation of disease-relevant toxic proteins. In parallel, we use biochemical and structural approaches to dissect mechanistically the various steps of the ERAD pathways. These strategies helped us in discovering ERAD mechanisms contributing to the homeostasis of the endoplasmic reticulum, the organization of the nuclear envelope and regulation of lipid metabolism. Although we focus primarily on fundamental aspects of protein quality control, our work will shed light on how these processes are disrupted in human disease and may ultimately contribute to better therapeutics.
You will be using techniques that are well established in our lab to study the mechanisms of protein degradation. The assays normally involve handling tissue culture cells, flow cytometry, basic protein biochemistry (western blotting and immunoprecipitation). You may also get some hands-on experience in molecular biology (cloning, PCR, etc).
Outcomes
You will contribute towards characterising a new effector or substrate of the ERAD pathway using biochemical and/or genetic tools.
Entry requirements
You should have, or be studying, a degree in life sciences and motivated to pursue scientific research in this study area.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Pathology 05
How cells ensure chromosome inheritance?
Supervisor
Professor Fumiko Esashi
Theme
Cellular and translational biology
Description
In many animals and plants, every chromosome contains a unique structural region, called the centromere. The centromere recruits the kinetochore machinery, which ensures proper segregation of chromosome when cells divide. Curiously, the centromeric DNA sequences are least conserved even between closely related species, but they are commonly composed of arrays of repetitive element in animals and plants. We study why and how these repeats have evolved at centromeres, with specific focus on the mechanism called homologous recombination (HR).
HR is an evolutionarily conserved mechanism that catalyses homology-directed repair of DNA breaks and is essential for cell survival. Surprisingly, our recent study has revealed that centromeres harbour unusually high levels of intrinsic DNA breaks even in non-cycling cells, driving hyper-recombination. Building on this observation, the project aims to elucidate how centromeric DNA breaks impact on the fitness of human cells.
Outcomes
You will learn to assess cellular phenotype, genetics and/or advanced imaging, depending on your primary interest, during the project. This will involve cell culture, microscopy and molecular biology. You will also gain a clear understanding of the research field of genome stability control and centromere biology.
Entry requirements
You should have, or be studying, an undergraduate degree in a life sciences or biosciences discipline.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Physiology
Physiology 01
How does in utero exposure to high temperature cause birth defects?
Supervisor
Professor Duncan Sparrow
Theme
Developmental biology
Description
Congenital heart disease (CHD) is the most common type of birth defect, affecting 0.8% of births in the UK. One-third of cases have a genetic cause, but the cause of the remainder is less certain. Some of these are caused by exposure of the developing embryo to environmental teratogens in utero.
During this project you will investigate the effects of one of the factors on embryonic development using a mouse model.
Exposure to elevated temperature (hyperthermia) during pregnancy in humans can significantly increase the risk of having a baby with CHD. This can occur due to maternal viral infection, or exposure to high environmental temperatures. However, we don’t know how hyperthermia causes CHD. You will help us investigate the morphological consequences of maternal hyperthermia on embryonic heart and craniofacial morphology using 3D imaging techniques.
You will then look for changes in gene and protein expression in hyperthermia-exposed embryos using molecular methods and confocal microscopy.
Outcomes
The project will give you an opportunity to learn several, highly transferable, lab techniques.
These include high resolution episcopic microscopy (HREM), immunofluorescence and RNAScope staining of mouse embryos, confocal imaging, and image analysis. You will also develop a good understanding of experimental design, and approaches to data analysis and interpretation. We hope that the results from this project will contribute to a publication.
You will also receive mentorship and guidance if interested in pursuing a DPhil or a future career in research.
Entry requirements
Whilst we aim to teach all relevant skills in our lab, you should have a background or strong research interest in developmental biology, molecular biology or both. You must also be comfortable with animal research.
Physiology 02
Modelling early-stage Alzheimer’s disease biology in fruit flies
Supervisor
Professor Clive Wilson
Theme
Cellular and translational biology
Description
Amyloidogenesis, the aggregation of specific proteins and peptides into fibrils, occurs normally, for example when hormones are packaged into secretory granules, and pathologically, in disorders like Alzheimer’s disease, where an abnormally cleaved product of the Amyloid Precursor Protein (APP) is secreted and forms amyloid plaques in late-stage disease.
Using a new cell model to study granule formation in the fruit fly, we have shown that APP controls the priming of normal protein aggregation into secretory granules at membrane surfaces. When pathological forms of APP are expressed, these aggregates fail to dissociate from membranes, leading to the late-stage defects seen in Alzheimer’s, but also much earlier events that reduce cell viability.
In this project, one of the genetic mechanisms that control these changes, which we hypothesise mirror previously unrecognised early pathological events in Alzheimer’s, will be characterised. The project will involve microdissection, genetics, live-cell fluorescence imaging and bioinformatics.
Outcomes
You will receive training in state-of-the-art approaches that we have developed to study the process of normal and pathological amyloidogenesis in living cells for the first time.
Your responsibilities will include contributing to the development of a new model for APP-induced cell degeneration that appears to involve other major players in Alzheimer’s like the Tau protein.
There will also be the opportunity for you to be involved in the characterisation of a novel mechanism by which early defects in Alzheimer’s disease are activated and start to work out with the research team how this mechanism could be suppressed.
Entry requirements
You should have, or be studying, a degree in any area of biological or biomedical sciences.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Physiology 03
Autophagic flux in Parkinson’s disease patient derived fibroblasts using LC3-Keima
Supervisor
Dr Brent Ryan
Theme
Cellular and translational biology
Description
Parkinson’s disease (PD) is the second most common neurodegenerative disorder, with clinical symptoms presenting with cardinal motor dysfunctions. Whilst PD is diagnosed by its clinical manifestations, post-mortem analysis of patients with PD have elucidated the neuropathology of the disease – progressive loss of dopaminergic neurons, accompanied by the accumulation of α-synuclein protein aggregates (Lewy Bodies) in neurons.
Studies have demonstrated that impaired recycling of damaged proteins (autophagy) may lead to this accumulation of α-synuclein, which can be measured by monitoring a protein called LC3.
For this project, we will create a genetic autophagy reporter that we can use in PD patient derived skin cells (fibroblasts). We can measure if autophagy is changed by fluorescent microscopy using a high-throughput imaging system. We may be able to then use this in a compound screen, which will potentially lead to new targeted PD therapeutics.
Outcomes
You will gain experience in several key cell biology techniques as well as being immersed in an active research environment. This will enable broadening of expertise, as well as help you derive a better understanding of studying and working in a research laboratory.
The project itself will allow for training in cell culture and aseptic techniques. You will also be trained on key molecular biology techniques such as cloning plasmids, as well utilising a very high-level imaging system, the Opera Phenix, including the robotics used.
There will also be the opportunity to work in a lab with a range of experience from junior to senior scientists who are willing to advise and mentor. If successful, the data and resources generated will have a real-world impact on ongoing research with you contribution credited accordingly if work with their direct involvement is published.
Entry requirements
You should have some previous experience in a biology-related discipline but no prior wet lab experience is necessary.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Physiology 04
The function of BMP signalling in lymphatic development and disease
Supervisor
Dr David Grainger
Theme
Cellular and translational biology
Description
The lymphatic vascular system is a network of vessels that drains interstitial fluid (lymph), traffics immune cells and absorbs lipids. Its critical roles in homeostasis is reflected by the diseases that arise from its perturbed development and misfunction in primary lymphedema where an accumulation of protein-rich fluid results in uncontrolled swelling.
Through studying the embryonic origins and molecular cues instructing normal lymphatic development we aim to identify undiscovered causes of primary lymphedema. We are currently focussed on the role of the Bone Morphogenic Protein (BMP) signalling pathway as we have identified TLL1, a modulator of BMP signalling as a potential causative gene in primary lymphedema. This project will combine histological and fluorescence imaging of TLL1 knock-out mice with biochemical assays of mutant human TLL1 in vitro to better understand TLL1’s function during development of the lymphatic system and how mutations in the gene could cause primary lymphedema.
You will perform confocal imaging of whole mount and/or sectioned (cryostat and vibratome) mouse embryonic tissue by staining with immunofluorescence and RNA-fluorescence in situ hybridisation (FISH) for components of the BMP signalling pathway. Additionally, you will transfect HEK-29T cells with plasmids expressing TLL1 wildtype or mutant forms in combination with BMP signalling components followed by quantification by western blot and RT-qPCR.
Outcomes
You will learn how to study developmental and vascular biology and have a good understanding of experimental design, frequently used methods, data analysis and interpretation. Data may potentially be included in a manuscript for publication.
Entry requirements
You should have, or be studying, a biological/life sciences undergraduate degree and an enthusiastic approach to learning about our field of study and experimental techniques.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Population Health
Population Health 01
Ethnic inequalities in health in the Malaysian cohort
Supervisor
Dr Jennifer Carter
Theme
Population health
Description
The top causes of death worldwide are cardiovascular and respiratory illnesses, but patterns of disease differ across ethnic groups and region. Multi-ethnic Malaysia provides an interesting environment to study ethnic inequalities in health as their population is comprised of large Malay, Indian and Chinese subgroups.
Depending on your areas of interest, projects could include the following:
- investigating ethnic differences in a range of diseases (such as heart disease, stroke, tuberculosis, depression, etc) in a subsample of 6,000 adults from The Malaysian Cohort study. After adjusting for socioeconomic differences between the ethnic groups (such as education, income, and access to health insurance), how much do the ethnic differences in the prevalence of disease diminish? (see similar paper); and
- investigating ethnic differences in the use of Complimentary and Alternative Medicine (CAM), in comparison to the use of modern treatment, on the control of hypertension (see similar paper).
Outcomes
You will produce a short research report that may have the opportunity to be submitted for publication in a global health peer-reviewed journal.
Entry requirements
You should have, or be studying, a degree in biology, biomedical sciences, medicine, statistics, public health or other related study area.
You should possess an interest in inequalities in health, global health, or cardiovascular disease.
This project will use quantitative data analysis, so experience of and/or an interest in statistics would be beneficial. If you have had no prior training in statistics, you will have the opportunity to learn these skills during the placement.
Population Health 02
Cancer epidemiology
Supervisor
Dr Christiana Kartsonaki
Theme
Health data science
Description
The aim of the project will be to study risk factors or biomarkers for certain types of cancer. The specific objectives can be adapted to match your interests and background. The project may involve a systematic review and meta-analysis, or other literature review and/or data analysis. For example, it may be a systematic review and meta-analysis on a particular risk factor and cancer type. Alternatively it could be on the analysis of a cancer-related dataset. You will learn how to search the literature, use the statistical software R to analyse data, plan research and perhaps write a protocol or analysis plan, and some epidemiological and statistical concepts and methods.
Outcomes
You may have the opportunity to contribute to a paper to be submitted for publication.
Entry requirements
You should have an interest in epidemiology, medicine, health, (bio)statistics or another related field.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Primary Care Health Sciences
Primary Care Health Sciences 01
Comparing media accounts with qualitative data about access to general practice appointments
Supervisor
Professor Catherine Pope
Theme
Health data science
Description
There is a crisis in general practice and getting (or rather, not getting) access to appointments with a GP is a significant concern for the public, patients, practitioners, service providers and policy makers. This crisis is represented in the UK media, often as evidence of wider problems facing the UK NHS. We are completing a major study of access to general practice appointments, that includes interviews with patients and observation of reception areas/appointment making.
We would like to extend this work by seeing how the media representations compare with the real-world experience.
In support of the project you will be expected to:
- read interview transcripts and observation notes, and papers/reports from the ongoing GP SUS study;
- search for and collect UK media accounts of accessing GP appointments; and
- select and apply an appropriate method of analysis to compare these two data sets (likely thematic and/or content analysis).
Outcomes
You will develop skills and understanding of coding and analysis in qualitative health research, secondary (text) data analysis, and experience of working with an applied health research team.
You will gain knowledge of access to general practice in the UK, and have the chance to develop report/paper writing skills, working with a larger project team. We will expect you to produce a report or journal paper or other relevant output to disseminate the learning about access to general practice - and to present this to colleagues in the Nuffield Department of Primary Care Health Sciences.
Entry requirements
You should have, or be studying, a degree in medicine, social sciences (eg sociology or anthropology), or where you have been dealing with textual data (eg English, history or media studies).
You need to have some aptitude for or interest in working with text based /qualitative data. Some prior knowledge of the UK NHS and general practice may be an advantage.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Primary Care Health Sciences 02
Cancer epidemiology: harnessing existing data to improve care and equity
Supervisor
Dr Diana Withrow
Theme
Health data science
Description
Electronic medical records from primary care are routinely linked to records from hospitals, the cancer registry, and death registrations and anonymised for research purposes. From this ‘big data’ one can explore what is and is not working efficiently and equitably in the health care system. You will work with the QResearch group, which holds a database including 35 million patients, to explore diagnostic pathways for cancer and their impact on survival. You are likely to contribute to evidence synthesis (literature review) and data analysis and interpretation, gaining a better understanding of health data and cancer epidemiology.
Outcomes
You may have the opportunity to contribute towards work which we hope to publish in a peer-reviewed manuscript.
Entry requirements
If possible, you should have some training in statistics, but this is not essential.
Primary Care Health Sciences 03
Can body temperature help to identify COVID and other new infections?
Supervisor
Dr Susannah Fleming
Theme
Health data science
Description
Pandemics preparedness will require diagnostic and screening strategies in advance of the development of disease-specific tests.
Body temperature, and in particular, fever, is a common and underrated symptom of infection. Body temperature has been used to screen for infection with new viral infections, but we do not fully understand how accurate this is. Understanding this may be vital when a new infection is identified. The current literature on the diagnostic value of body temperature is mixed, and our colleagues at the National Physical Laboratory hypothesise that suboptimal temperature measurement in current studies may explain this variation.
You will carry out a systematic search to comprehensively review existing studies on how well body temperature can identify new respiratory viruses and appraise the studies under the guidance of subject experts, and will summarise the results in a report.
Outcomes
You may have the opportunity to contribute to publishing the results of this work in a peer-reviewed journal and possibly to be named as a co-author for this work. The results will also feed into future research on body temperature and may inform responses to future pandemics.
Entry requirements
This project is most suitable for applicants from a scientific or numerate discipline. You should have an interest in infectious diseases.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Primary Care Health Sciences 04
The management of patient risk in calls to NHS 111 about COVID-19
Supervisor
Professor Rebecca Barnes
Theme
Health data science
Description
You will contribute to a retrospective study of the telephone management of people who were affected or at risk of COVID-19 infection via NHS 111 services led by the primary supervisor. The call recordings were made in April 2020 in England at the start of the UK COVID-19 pandemic.
You will be involved in the management and analysis of a unique dataset of over 250 call recordings already collected and transcribed verbatim, and associated records data. The aim of the project will be to identify and code instances of risk management activities such as risk assessment, risk communication and risk prevention.
Outcomes
Skills developed will include how to work with sensitive data, qualitative health research methods and basic descriptive statistics. You will also gain knowledge of the management of a public health crisis, plus experience of preparing the work for potential presentation +/- publication and working with a larger project team.
Entry requirements
You need to have an aptitude for or interest in working with recordings and transcripts of health care interactions between patients and health care professionals, or health records data.
Suitable degree subjects include medicine, nursing, paramedic science, social sciences (eg sociology, psychology, communication studies, anthropology) or humanities (eg linguistics, English).
Basic skills in qualitative research methods and/or audio editing software would be an advantage.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
Surgical Sciences
Surgical Sciences 01
Validation of molecular profiles of donor kidney quality in organ transplantation
Supervisor
Dr Letizia Lo Faro
Theme
Transplant biology
Description
The transplant community needs tools to assess organs donated for transplant. These tools are necessary to help predict how the organs will function in the recipient and/or whether they can benefit from therapeutic interventions before transplant. By applying these tools, we will be able to increase the number of organs transplanted and make sure they last for longer.
In our previous work we have found molecules (proteins) in donated kidneys that are associated with poor outcomes (poor function) following transplantation. We now want to validate these associations in a larger group of samples. Using clinical samples collected in a large transplant tissue bank (Quality In Organ Donation, QUOD), we aim to study whether our proteins of interest are associated with poor kidney outcomes, specifically in the case of kidneys donated from more injured donors. We will analyse these samples using mass spectrometry-based protein analysis and immunoassay, such as ELISA.
Outcomes
Throughout this project you will help us to identify clinical markers of organ quality. You may have the opportunity to contribute to scientific publications as well as presentations and dissemination of knowledge at research meetings.
Entry requirements
A basic knowledge of biochemistry and protein analysis would be beneficial.
Funding information
This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,670 before tax and National Insurance contributions). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.
