Research Associate in Dynamic Cancer Risk Prediction (Epidemiologist/Statistician/Health Data Scientist) (Fixed Term)
An exciting opportunity has arisen for a new analytical role to join the Cancer Data Driven Detection (CD3) programme in the Department of Public Health and Primary Care at the University of Cambridge. The post‑holder will contribute to the development and validation of dynamic cancer risk prediction models that address challenges of missing and incomplete data within large‑scale electronic health records (EHRs).
CD3 is a new, multidisciplinary and multi‑institutional strategic national research programme dedicated to using data to transform our understanding of cancer risk and enable early interception of cancers. It represents a major, multi‑million‑pound flagship investment funded through a strategic programme award by Cancer Research UK, the National Institute for Health and Care Research (NIHR), Engineering and Physical Sciences Research Council (EPSRC), and the Peter Sowerby Foundation; in partnership with Health Data Research UK (HDR UK) and the Economic and Social Research Council’s Administrative Data Research UK programme (ADR UK).
Post‑holder will develop and apply advanced statistical and machine learning approaches to model how cancer risk estimates evolve as new clinical information becomes available (for example, evolving symptoms or new test results). A key focus will be on understanding and mitigating the impact of missing data – arising from variations in clinical recording practices, data completion and patient engagement – on model performance and fairness. The role will also involve evaluating model calibration, discrimination and fairness across patient sub‑groups, ensuring models are robust and equitable for clinical implementation.
Post‑holder will work closely with a multi‑disciplinary team including Angela Wood (Cambridge), Matthew Sperrin (Manchester), Gary Abel (Exeter) and Montserrat Garcia‑Closas (Imperial College London), bringing together expertise in biostatistics, health data science and cancer epidemiology. They will also engage with colleagues across the wider CD3 community, patient and public contributors and equality, diversity and inclusion partners to ensure model transparency and societal relevance.
Post‑holder will have access to extensive linked EHR resources and high‑performance computing facilities, and will contribute to the development of open, reproducible analysis pipelines. There will be opportunities to expand into related areas depending on candidate’s expertise and interests, such as exploring fairness‑aware AI models, generative data imputation methods or simulation‑based model validation. The work is expected to lead to several high‑impact publications and presentations at national and international conferences.
Preferred candidate will have a PhD (equivalent) in Statistics, Biostatistics, Data Science, Epidemiology, Computer Science or another relevant quantitative discipline. They should have a strong background in statistical or machine‑learning methods and experience working with large‑scale health data. Proficiency in R or Python is essential. Experience of handling missing or incomplete data, developing or validating risk prediction models, or knowledge of algorithmic fairness would be advantageous.
Post‑holder should have excellent communication and organisational skills and the ability to work effectively within a multidisciplinary research environment. This role offers an excellent opportunity to develop an independent research profile within one of the UK’s leading centres for population health data science.
Appointment and Funding
- Appointment at Research Associate is dependent on holding a PhD (including those who have submitted but not yet received a PhD). Until the PhD is awarded, appointment will initially be at research assistant level and amend to research associate when the PhD is awarded.
Funding available for 36 months. Position is full‑time; we welcome applications no less than 0.5 FTE.
Application Details
Please ensure you upload a covering letter and CV. Covering letter should outline how you match the criteria for the post and why you are applying for this role. Include details of your referees, including e‑mail address and phone number, one of which must be your most recent line manager.
Please quote reference RH48046 in your application and in any correspondence about this vacancy.
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.
Contact
Informal enquiries: Professor Angela Wood –
