Medical and Health Care Studies: Fully Funded PhD Studentship in Medical and Health Care Studies (RS907)

Interviews: Week 8-12^th December 2025

The Faculty of Medicine Health and Life Science is pleased to announce THREE fully funded PhD scholarships in fields related to mathematical and statistical methods to advance medical research.  We ask applicants to choose ONE project from the list of FOUR below when applying. Students will be shortlisted for interview across the four projects and will be required to give a short presentation on why they have chosen that project and why their academic experience makes them a suitable candidate. Then following interview, the top student in each of the FOUR projects will be ranked and the top THREE successful applicants chosen. There will be a maximum of ONE student per project. This process will ensure an excellent fit of student to project and also an excellent strategic fit of the project within the faculty.  

Project titles:

Bayesian methods for image clustering applied to population health research

Novel statistical approaches for analysing high resolution movement data in animal models of human health

Early presentation of atherosclerotic cardiovascular disease in patients with depression and influence of cardiovascular risk factors 

Longitudinal modelling to integrate biological and physiological responses with physical activity in healthy and adverse pregnancy

Funding providers:  St David’s medical Foundation

Start date:  1 January 2026

Supervisors:  Project 1: Dr James Rafferty Project 2: Dr Emma Kenyon Project 3: Dr Libby Ellins Project 4: Prof Cathy Thornton

Aligned programme of study: PhD Medical and Health Care Studies

Mode of study: Full time

Project 1: Big, routine datasets often contain rich group structure. Models accounting for this structure are called hierarchical, multi-level or mixed effects models, but require knowledge of groups in the data. Unobserved group structure can be revealed using clustering methods such as mixture modelling. This studentship seeks to develop a new and innovative model where images are used to find clusters in data and analyse them conditioned on an outcome. We expect the studentship to start with a review of relevant methodological literature. There will be a period of model development and testing using a simulation study followed by a pilot application of the model to healthcare imaging data. This pilot will be performed using data administered by the Alzheimer's Disease Neuroimaging Initiative (ADNI), which contains personal and imaging data on dementia patients in the United States. The project would suit someone with a background in statistics or mathematics.

Project 2: Vestibular defects underlie balance disorders estimated to cost >£2.3 billion every year in the UK. Animal models are used to understand the genetic basis of auditory and vestibular systems, with high frame-rate video used to associate genotypes with movement signatures. The challenge is to develop modern statistical modelling for complex research questions using high-resolution movement tracking data.  Our research question is whether we can detect balance disorders in both zebrafish and mice. You will develop cutting edge mathematical and statistical modelling from the field of movement ecology with the goal of developing methodologies that are generalisable across clinical and animal model settings. This will give excellent employability skills within and beyond academia, from biotech start-ups to the international pharmaceutical industry.

You will either have a mathematical background with an interest in real world human health application or a biological/biomedical background with an aptitude for data analysis. The opportunities you will get through this project include advanced training in modelling across a range of languages (R, Python, C++) and optional experience acquiring data from zebrafish with vestibular defects. This project is a rare opportunity to make a fundamental advance in animal movement analysis.

Project 3: Can you help us use big data to gain a better understanding of why people with mental health conditions develop cardiovascular disease earlier than those without?

Patients with depression are at increased risk of developing atherosclerotic cardiovascular disease (ASCVD) a leading cause of morbidity and mortality.  We have shown that patients with depression develop ASCVD younger (11.5 years) than non-depressed. Depression has a broad definition and subtypes with differential clinical and pathophysiological profiles which may contribute to earlier development and presentation of ASCVD. This project will explore the early presentation of ASCVD in patients with depression and look to identify those patients at greatest risk.

The project will use population level, electronic health records and longitudinal data to identify those depressed patients at greatest risk of developing ASCVD and identification of contributing risk factors, using survival models with time varying parameters. Markov processes will be used to consider the bi-directional nature of the relationship to determine the impact of the condition on mental health.

This approach will help us to get a better perspective of how factors interact to direct efforts to close the gap in the development of ASCVD between those with and without depression.

Project 4: Pregnancy involves profound physiological adaptations, yet maladaptation can lead to adverse outcomes such as pre-eclampsia or preterm birth. Understanding how environmental exposures and physical activity influence these responses is crucial for improving maternal health. This PhD project will develop innovative mathematical and statistical models to characterise dynamic physiological changes across healthy and complicated pregnancies. Using recently collected wearable datasets that integrate cardiovascular and activity measures with omics data, air quality, and environmental factors, the student will build a versatile computational framework for feature extraction, data integration, and dimensionality reduction. Advanced trajectory inference and machine learning approaches will be applied to identify digital twins that capture key physiological mechanisms.

The project will provide novel insights into (patho)physiological adaptations during pregnancy and establish tools for analysing complex longitudinal datasets. Outcomes will inform the development of diagnostic and monitoring technologies, with applications extending beyond pregnancy to broader health and disease contexts.

PhD: Applicants for PhD must hold an undergraduate degree at 2.1 level and a master’s degree. Alternatively, applicants with a UK first class honours degree (or non-UK equivalent as defined by Swansea University) not holding a master’s degree, will be considered on an individual basis.

English Language
IELTS 6.5 Overall (with no individual component below 6.5) or Swansea University recognised equivalent. Full details of our English Language policy, including certificate time validity, can be found here.

Note for international and European applicants: details of how your qualification compares to the published academic entry requirements can be found on our Country Specific Entry Requirements page.

This scholarship covers the full cost of tuition fees and an annual stipend at UKRI rate (currently £20,780 for 2025/26).

To apply, please complete the entire application form 

In order to be considered for this scholarship award the following steps are also required.

1) In section ‘Programme Related Information’ please input the relevant RS Code for the scholarship award i.e. RS907

2) In section ‘Research’ you will see ‘Proposed project title/studentship title’* (Mandatory)

• In ‘Proposed project title/studentship title’ please input both:

• the RS Code, RS907 and
• the scholarship title.

• Please leave Proposed Supervisor field blank
• Please leave Research Project (if applicable) blank
• In ‘Do you have a proposal to upload?*’(Mandatory) Please select Yes
• Then upload copy of your proposal so you can be matched to one of the 4 projects outlined in the advertisement

3) In section ‘Funding information’ please choose the option ‘Scholarship Funding’ only. Please ensure no other options are selected.

*It is the responsibility of the applicant to list the above information accurately when applying, please note that applications received without the above information listed will not be considered for the scholarship award.

If you’ve previously applied for this programme, the system will display an “Application Submitted” warning and block a new submission. In this case:

1. Apply for the same course with the next available start date (e.g., select January if October is unavailable).
2. Email pgrscholarships@swansea.ac.uk with your student number and the relevant scholarship RS code, requesting the start date be amended to match the advert.
3. Admissions staff will then update your application accordingly.

One application is required per individual Swansea University led research scholarship award; applications cannot be considered listing multiple Swansea University led research scholarship awards.

NOTE: Applicants for PhD/EngD/ProfD/EdD - to support our commitment to providing an environment free of discrimination and celebrating diversity at Swansea University you are required to complete an Equality, Diversity and Inclusion (EDI) Monitoring Form in addition to your programme application form.  

Please note that completion of the EDI Monitoring Form is mandatory; your application may not progress if this information is not submitted.

As part of your online application, you MUST upload the following documents (please do not send these via email):

• Research Proposal
• CV
• Degree certificates and transcripts (if you are currently studying for a degree, screenshots of your grades to date are sufficient)
• A cover letter including a ‘Supplementary Personal Statement’ to explain why the position particularly matches your skills and experience and how you choose to develop the project.
• One reference (academic or previous employer) on headed paper or using the Swansea University reference form. Please note that we are not able to accept references received citing private email accounts, e.g. Hotmail. Referees should cite their employment email address for verification of reference.
• Evidence of meeting English Language requirement (if applicable).
• Copy of UK resident visa (if applicable)
• Confirmation of EDI form submission

Informal enquiries are welcome; please contact:

Dr James Rafferty J.M.Rafferty@Swansea.ac.uk

Dr Emma Kenyon Emma.Kenyon@Swansea.ac.uk

Dr Libby Ellins E.A.Ellins@Swansea.ac.uk

Prof Cathy Thornton C.A.Thornton@Swansea.ac.uk

*External Partner Application Data Sharing – Please note that as part of the scholarship application selection process, application data sharing may occur with external partners outside of the University, when joint/co- funding of a scholarship project is applicable.

** In exceptional circumstances, and subject to the discretion of the University and/or the relevant funding body, a deferral of offer may be granted to the next available enrolment period. Such deferral will typically not exceed a duration of three calendar months from the originally stipulated commencement date. Please note that only one deferral may be considered, and any such deferral is not guaranteed.