About this position
Fully Funded PhD Studentship (Home Fees)
Project Title: Developing Fair and Robust Cancer Risk Prediction Models Using Health Data
Supervisor: Professor Angela Wood (Professor of Biostatistics and Health Data Science)
Start date: January 2027 (or earlier)
We are offering an exciting fully funded PhD studentship at the University of Cambridge, embedded within Cancer Data Driven Detection (CD3) - a major £10 million national research programme bringing together leading experts in cancer epidemiology, biostatistics, artificial intelligence, and health data science.
THE PROJECT: Early cancer diagnosis remains challenging, particularly for patients with vague or non-specific symptoms. Electronic health records provide unprecedented opportunities to develop personalised cancer risk prediction models, but missing and incomplete symptom data can introduce bias and worsen existing inequalities.
This PhD will develop innovative statistical and machine learning approaches to understand, model, and overcome missing data in large-scale healthcare datasets. The student will investigate how data completeness varies by patient and healthcare setting, evaluate its impact on cancer risk prediction, and develop new methods to produce more accurate, equitable, and clinically useful models.
The project offers opportunities to work with large linked electronic health records, advanced statistical methods, simulation studies, and reproducible open-source software development.
TRAINING AND RESEARCH ENVIRONMENT: The student will join the Department of Public Health and Primary Care and become part of the multidisciplinary CD3 consortium, collaborating with world-leading researchers across the UK, including teams at Manchester, Exeter and University College London.
The student will receive training through the Cancer Early Detection Training Programme, delivered in partnership with the Alliance for Cancer Early Detection (ACED), alongside tailored supervision and development in biostatistics, health data science, cancer epidemiology, and responsible AI.
WHO SHOULD APPLY? We welcome applications from highly motivated candidates with a first-class or upper second-class degree (or equivalent) in a quantitative discipline such as statistics, mathematics, computer science, engineering, data science, or a related biomedical/population health subject.
Applicants should have strong analytical and programming skills (e.g. R or Python), an interest in health data science, and enthusiasm for interdisciplinary research aimed at improving patient outcomes.
Applicants must meet the University of Cambridge entrance requirements: see https://www.postgraduate.study.cam.ac.uk/application-process/entry-requirements
FUNDING: This studentship is available to applicants eligible for Home tuition fees and includes a tax-free stipend at the UKRI 2026 rate (currently £20,780 per year).
HOW TO APPLY: Applications should be made through the PhD in Public Health and Primary Care (full-time) at the University of Cambridge. To apply please click the above 'Apply' button.
Please quote reference RH50039 on your application and in any correspondence about this vacancy
In order to apply for this opportunity, you will need:
- Details of two academic referees (references will be taken up immediately).
- Transcript(s)
- CV/resume
- Evidence of competence in English
- Statement of Interest outlining your suitability, why you are interested in a PhD in this area, your background and research interests
Applications will be considered until 1 October 2026 .
Applications will be reviewed on a rolling basis, and suitable candidates may be invited for interview as applications are received. Early application is therefore strongly encouraged, as the studentship may be filled before the closing date.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.
Funding
GBP 20,780 per year
How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.