What this PhD programme offers

HDR UK is funding a series of PhDs with leading UK universities and research organisations. This brand new programme offers the chance to carry out a doctoral research project at the leading edge of health data science.

Each project has been selected for its scientific excellence, importance and originality and will help deliver the aims and objectives of HDR UK’s Big Data for Complex Diseases (BDCD) Driver Programme.

Graduates will be well placed for a career at the forefront of health data research.

Available research project

  • Modelling the Impact of Diagnostic Pathways in Cancer & Cardiovascular Disease. University of Swansea

See below for details of this project and to apply.

Who the PhD projects are for

The programme is for enthusiastic, talented students who want to use data-driven research to develop and shape the UK’s response to the most complex health challenges of our times.

  • For eligibility details see the information about each PhD project.
  • HDR UK is committed to promoting equality and inclusion, click here to find out more.
  • We aim to accommodate specific needs and personal circumstances. Please make us aware of individual circumstances when applying or contact us directly at learn@hdruk.ac.uk. Please note the HDR UK PhD Student Privacy Notice.

If you have questions or require adjustments to the application process, please contact learn@hdruk.ac.uk.

Outcomes and benefits

This is a chance to earn a PhD from a leading university and by conducting a research project that will make a direct contribution to improving the health and care of patients.

Three or four years of funding will be available from HDR UK depending on the project. A three-year research project will be carried out at your home university and will be linked to HDR UK Big Data for Complex Disease Driver Programme aims and objectives.

Students will receive excellent training and career development opportunities in an environment that nurtures team science.

They can be hosted by a single research organisation or part of the studentship can be hosted by a second research organisation.

Each BDCD studentship award will comprise a three-year stipend and research costs of maximum £75,000 per studentship awarded to the host organisation(s). This is based on UK Research and Innovation (UKRI) minimum rate (Appendix B).

Benefits include:

  • Tax-free stipends with annual increases based on UKRI advertised rates
  • Fully-paid tuition fees (and college fees where required) – international fee waivers are not normally available – check project information
  • Research costs of up to £5,000 a year
  • Expenses and travel costs for conferences and events £300 a year.


Application forms are available underneath the information about each of the projects. Studentships will generally begin in October 2023 or January 2024.

BDCD Driver Programme aims and objectives

For a wide range of complex diseases, deriving intelligence from nationwide, multisource, linked health relevant data has the potential to yield crucial insights that accelerate and enhance opportunities for innovation in disease detection, diagnosis, treatment, improved care, better outcomes and more rational health policy.

Cancer and cardiovascular diseases (CVD) are the two commonest causes of morbidity and mortality in the UK and globally, with incidence, morbidity and mortality increasing over the last several decades as the world’s population has aged.

Slowing these global trends requires approaches that recognise and exploit the power of whole, large population-scale health relevant data to catalyse health data science and its translation.

We also need to break down traditionally siloed disease and expertise specific domains, rising to the challenge of jointly addressing cancer, CVD, other complex diseases, their inter-relationships and their sequelae.

Crucially, we need to use the intelligence gained to translate into real benefit for citizens and patients and influence national and international policy and best practice.


The PhD Projects

  • Key details

    • Hosted by: University of Swansea
    • Lead supervisor: Dr Rhiannon Owen, Swansea University Medical School
    • Duration: Three years
    • Stipend: UKRI stipend rates apply
    • No international fee waivers
    • Start date: Flexible for the right candidate

    Project summary

    Many patients who are diagnosed with cancer and/or cardiovascular disease (CVD) receive their diagnosis in Accident and Emergency (A&E). These patients tend to have more severe disease than those who receive a diagnosis from their general practice (GP).

    This PhD project will develop mathematical models to predict what the benefits of receiving an earlier diagnosis (via their GP) would have been for both patients and the NHS. With the frequent co-existence of cancer and CVD, this presents an important opportunity to improve population health, and reduce potential health inequalities, by improving the diagnostic pathway of both diseases.

    The project will specifically explore the epidemiology of cancer and/or CVD diagnoses, especially with respect to where, and for whom, such diagnoses are made, using population-scale data including 2.9 million individuals in the Secure Anonymised Information Linkage (SAIL) Databank Wales Multimorbidity e-Cohort.

    The project will use statistical modelling and machine learning techniques to predict the impact of in-hospital diagnoses for cancer and/or CVD on patient-relevant and NHS outcomes, especially with regards to life-expectancy and quality of life. In doing so, it will provide both a benchmark against which existing diagnostic/pathway initiatives can be evaluated, as well as identifying potential inequalities and predicting the impact of new areas of system development to improve patient outcomes.

    Eligibility and suitability

    Applicants will need an MSc in Statistics/Biostatistics or Epidemiology/Health Data Science (with a strong analytical component) plus programming and data analysis skills/experience in R and/or Python.

    Experience of analysing large-scale linked electronic health record data Knowledge of Bayesian methods would be an advantage.

    Click here for further details and to access the application form.

    • If you have any questions contact R.K.Owen@Swansea.ac.uk or Keith.Abrams@warwick.ac.uk

Research projects underway

The programme already has several projects underway. These are:

  • Advancing primary prevention strategies for complex diseases: University of Cambridge
  • BLOod Test Trend for cancEr Detection (BLOTTED): an observational and prediction model development study using English primary care electronic health records data: University of Oxford
  • Expediting the diagnosis of cancer and other diseases where early diagnosis can improve clinical outcomes in patients presenting to a GP with new symptoms: UCL

PhD Programme team

The core team for the programme includes:

  • Director: Prof Cathie Sudlow (HDR UK)
  • Director: Prof Mark Lawler (Queen’s University Belfast, HDR UK Northern Ireland)
  • Dr Rhiannon Owen, Associate Professor of Statistics, Population Data Science, Swansea University
  • Prof Julian Halcox, Professor of Cardiology & Health Data Science, Population Data Science, Swansea University
  • Prof Georgios Lyratzopoulos, Professor of Cancer Epidemiology (principal supervisor, healthcare epidemiology), UCL
  • Prof Spiros Denaxas, Professor of Biomedical Informatics (computational phenotyping), UCL
  • Dr Matthew Barclay, Senior Research Fellow / Cancer Research UK-ACED Fellow, Statistician (methodological lead), UCL
  • Ms Becky White, Data Science Research Fellow (data management), UCL
  • Dr Meena Rafiq, Clinical Fellow / Academic General Practitioner (clinical input into phenotyping and outcome selection/interpretation, UCL
  • Prof Angela Wood, Professor of Health Data Science, University of Cambridge
  • Dr Brian D. Nicholson (GP and NIHR Clinical Lecturer), University of Oxford
  • Prof Eva Morris (Prof of Health Data Epidemiology): University of Oxford
  • Dr Pradeep S. Virdee (Medical Statistician), University of Oxford
  • Emanuele Di Angelantonio (Professor of Clinical Epidemiology), University of Cambridge
  • Antonis Antoniou (Professor of Cancer Risk Prediction), University of Cambridge
  • Dr Rachel Denholm, University of Bristol
  • Dr Sophie Eastwood, UCL
  • Prof Jonathan Sterne, University of Bristol
  • Prof Nish Chaturvedi, UCL
  • Prof Kate Tilling, University of Bristol

The wider leadership team consists of subject matter experts from across the UK.