Health Data Science, Medical Statistics: Fully Funded Studentship in Population and Health Data Science (RS863)

Funding providers:  MRC/NIHR 

Subject areas: Health Data Science / Medical Statistics 

Project start date: October 2025 ** (Please see the note below regarding potential later start dates)

Supervisors: Dr James Rafferty & Professor Rhiannon Owen 

Aligned programme of study: Population and Health Data Science 

Mode of study: Full-Time 

Project description: 
The Child and adolescent Health Impacts of Learning Indoor environments under net zero (CHILI) Hub is a program funded by the MRC and NIHR, the goal of which is to understand the health effects we can expect to see as the UK transitions to net-zero. It is a collaboration between University College London, Imperial College London, The London School of Hygiene & Topical Medicine, Swansea University, the University of Leeds, the University of York and the UK Health Security Agency.  

As the climate changes, the impact of air pollution on child and adolescent health is one of the most complex and challenging problems in health data science. Air pollution is composed of several different environmental pollutants, for example particulate matter (PM10 and PM2.5), ozone (O3), nitrogen dioxide (NO2) and sulphur dioxide (SO2) are commonly measured. Each pollutant is produced and destroyed by different processes, and the levels of the various pollutants are correlated with each other, for example, and increase in NO2 causes an increase in O3. Statistical methodological developments are required to understand the relationship between air pollutants and health outcomes to account for both the size and nature of the data.  

Our response to climate change will change the composition of air pollution factors children and adolescents are exposed to. Many air pollutants are expected to reduce, but some may increase, for example, PM10 which is generally caused by acceleration and deceleration of road vehicles and is proportional to vehicle weight may increase due to the transition to electric cars, which are typically heavier than their fossil fuel powered counterparts. A framework that can accurately model complex dynamics and generate projections for future scenarios is essential for understanding the impact of changes to air pollution in the future, and planning further policy changes.  

This PhD project will develop statistical modelling frameworks that are able to handle large-scale, complex, and correlated time series data, and apply these frameworks to population-scale datasets to generate new knowledge and insights about the impact of different air pollutants on child and adolescent health. This may include techniques like generalisations of Autoregressive Integrated Moving Average (ARIMA) models, Dynamic Linear Models (DLM) and joint longitudinal and survival models. To appropriately capture uncertainty for health policy decision-making, these methods will be developed using a  Bayesian framework. 

This PhD project will deliver a substantial contribution to original research in the area of health data science and statistical modelling of population-scale data. We expect that findings will contribute to policy on the transition to net zero and air pollution. The successful candidate will develop important skills in data linkage and advanced analysis from a world class supervisory team, and benefit from the broad collaboration of the CHILI Hub. 

UK Fee-Eligible Students Only 

Due to funding restrictions, this scholarship is open to applicants eligible to pay tuition fees at the UK rate only, as defined by UKCISA, please see - Full list of categories for HE in Wales 

Degree requirements for PhD: Applicants must have obtained at least a 2:1 undergraduate degree in a relevant subject area (or non-UK equivalent as defined by Swansea University). Applicants are also expected to hold a relevant master's degree in Statistics/Biostatistics or Epidemiology/Health Data Science (with a strong analytical component). Programming and data analysis skills/experience in R and/or Python is desirable.  As is experience in Bayesian methods. Non-UK qualification, please see - Country Specific Entry Requirements page. 

English Language: IELTS 6.5 Overall (with no individual component below 6.5) or Swansea University recognised equivalent. For further information, please see the Swansea University English Language Entry Policy. 

If you have any questions regarding your academic or fee eligibility based on the above, please email pgrscholarships@swansea.ac.uk with the web-link to the scholarship(s) you are interested in.

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: Complete your application here in Learner Gateway

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. RS863 

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 RS863
  • 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 advert (you can save the advert by clicking print, and then print to pdf) 

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. 

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): 

• 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  

*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.