This four-year cohort-based training programme offers opportunities for doctoral study in computational statistics, machine learning and data engineering within the context of ethically-responsible health research.

It is jointly run by a range of Oxford departments including Computer Science, Statistics, Engineering Science, the Nuffield Department of Medicine, and the Nuffield Department of Population Health.

The course plans to admit students in 2024-25, but funding is not yet confirmed. In the unlikely event of the course not running for entry in 2024-25, applicants who have paid an application fee for this course and have not applied to any other courses under the applying to related courses application fee waiver scheme, will receive a refund of the application fee.

Course structure

The course begins with a training year, which consists of two terms of intensive training in core data science principles and techniques followed by a third term where you will undertake two eight-week research projects in two of your chosen research areas.

The taught courses covering core subjects such as computational statistics, machine learning, data engineering, ethics and governance, and health research methodology include the following:

  • Ethics
  • Software Engineering
  • Statistical Methods
  • Research Methods
  • Machine Learning
  • Bayesian Statistics
  • Medical Imaging
  • Biomedical Image Analysis
  • Biomedical Time Series Analysis
  • Device and Sensor Data
  • Genetics
  • Infectious Diseases
  • Modelling for Policy Making
  • Data Governance
  • Data Engineering
  • Health Data Quality
  • Health Data Standards
  • Data-driven Innovation.

Graduate destinations

It is expected that graduates will be well placed to take on leading roles in industry, academia and the public sector, including areas where health and health care data is used to direct policy or make decisions about patient care.

Entry requirements

As a minimum, applicants should hold or be predicted to achieve the following UK qualifications or their equivalent:

  • a first-class or strong upper second-class undergraduate degree with honours

This should be achieved in one of the following:

  • Mathematics
  • Statistics
  • Engineering Science
  • Computer Science; or
  • A related field with substantial mathematical background

A master’s qualification in one of the above subjects is recommended, but not essential.

For applicants with a degree from the USA, usually the minimum GPA sought is 3.5 out of 4.0.

If your degree is not from the UK or another country specified above, visit our International Qualifications page for guidance on the qualifications and grades  that would usually be considered to meet the university’s minimum entry requirements.

Other qualifications, evidence of excellence and relevant experience

  • Research or working experience in a relevant field may be an advantage.
  • Whilst not required, or expected, publications demonstrating previous research experience in a relevant field and a track record demonstrating an interest in research are likely to advantage your application.


You will need to register three referees who can give an informed view of your academic ability and suitability for the course. The How to apply section of this page provides details of the types of reference that are required in support of your application for this course and how these will be assessed.

Initiatives to improve access

This course is taking part in a continuing pilot programme to improve the selection procedure for graduate applications, in order to ensure that all candidates are evaluated fairly.

For this course, socio-economic data (where it has been provided in the application form) will be used to contextualise applications at the different stages of the selection process. Further information about how we use your socio-economic data can be found in our page about initiatives to improve access to graduate study.

This is also one of the courses participating in the Academic Futures programme, including the Black Academic Futures programme. Applicants who are offered a place on this course and meet the eligibility criteria will subsequently be considered for funding through the Academic Futures programme.