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University of Cambridge

PhD Studentship: Developing Fair and Robust Cancer Risk Prediction Models Using Health Data

Closes:

Salary: £20,780 per year

2,363

Fully Funded PhD Studentship (Home Fees)

Department of Public Health and Primary Care, Cardiovascular Epidemiology Unit, University of Cambridge

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 visit https://www.postgraduate.study.cam.ac.uk/courses/directory/cvphpdhpc and click 'Apply Now'.

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.

Job details

Title
PhD Studentship: Developing Fair and Robust Cancer Risk Prediction Models Using Health Data
Employer
University of Cambridge
Location
Cambridge, United Kingdom
Published
Jun 30, 2026
Closes:
Oct 1, 2026
Job type
Full time, Student / Phd Jobs
Field
Postgrad Student Opportunity
Salary

Salary: £20,780 per year

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Frequently Asked Questions

🎓What qualifications are required for this PhD studentship?

Applicants need a first-class or upper second-class degree (or equivalent) in a quantitative discipline such as statistics, mathematics, computer science, data science or a related biomedical subject. Strong analytical and programming skills (R or Python) are essential. See our guide: how to write a winning academic CV.

💰What funding is included in this PhD studentship?

This is a fully funded PhD studentship covering Home tuition fees plus a tax-free stipend at the UKRI 2026 rate (currently £20,780 per year). Only applicants eligible for Home fees can apply.

📝How do I apply for this PhD at University of Cambridge?

Apply via the PhD in Public Health and Primary Care programme. Quote reference RH50039. Required documents include two academic referees, transcripts, CV, English competence evidence and a Statement of Interest. Applications close 1 October 2026 and are reviewed on a rolling basis.

🔬What is the focus of this cancer risk prediction PhD project?

The project develops fair and robust cancer risk prediction models using large-scale electronic health records. It focuses on handling missing and incomplete symptom data, reducing bias, and improving equity in cancer early detection within the CD3 consortium.

🏫What training and research environment is provided?

Students join the Cancer Data Driven Detection (CD3) programme and receive training through the Cancer Early Detection Training Programme (with ACED). Collaboration spans Manchester, Exeter and UCL with emphasis on biostatistics, responsible AI and open-source software.
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