Research Associate* in Cancer Risk Modelling (Fixed Term)

Research Associate* - £37,694 - £46,049

Research Assistant - £33,002 - £35,608 

We are seeking a highly motivated and skilled Research Associate to join the Cancer Data-Driven Detection (CD3) programme. CD3 is a new, multidisciplinary and multi-institutional national research initiative dedicated to using data to revolutionise 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 from Cancer Research UK, the National Institute for Health and Care Research (NIHR), the Engineering and Physical Sciences Research Council (EPSRC), Health Data Research UK (HDR UK), the Economic and Social Research Council’s Administrative Data Research UK (ADR UK) programme, and the Peter Sowerby Foundation. 

The Research Associate will contribute to CD3 by developing and applying advanced epidemiological, statistical, and AI-based approaches to improve prediction of cancer risk across multiple tumour types. The post will be based at the Centre for Cancer Genetic Epidemiology (CCGE) in Cambridge but will involve co-mentoring and close collaboration with investigators across multiple institutions, reflecting the highly collaborative nature of the programme. Based within the Multi-Cancer Risk Prediction Driver Programme, the postholder will develop and validate novel multi-cancer risk prediction models using population-scale, multimodal datasets, including electronic health records, administrative data, and multi-omic data. 

Key priorities include:

• Advancing methods for multi-cancer risk prediction to account for correlations, competing risks, and pleiotropic effects.
• Developing data domain-specific multi-cancer risk prediction models.
• Integrating individual multifactorial cancer models into robust, equitable, and generalisable multi-cancer prediction tools. 

The postholder will play a central role in developing new methodology where best practice is currently unclear, and in evaluating model performance and transferability across diverse datasets and populations.

Applicants should have:

• A PhD (or near completion*) in epidemiology, biostatistics, statistics, applied mathematics, computer science, artificial intelligence, or a related discipline.
• Expertise in statistical modelling and/or machine learning, with experience applying advanced methods to complex, large-scale health or administrative datasets.
• Proficiency in R or Python.
• Excellent communication skills, with the ability to present complex data to both technical and non-technical audiences.
• A proven ability to collaborate effectively across institutions and disciplines.

Highly desirable experience includes risk prediction modelling (including survival analysis, competing risks, or multivariate outcomes), and working with population-scale health data such as electronic health records, cohort studies, or multi-omic datasets.

Additional Information

The CCGE and Department are committed to supporting hybrid working, but staff are expected to work onsite on a regular basis to foster collaboration and community.

This is a full-time position. We do welcome applications from those wishing to work part-time of no less then 0.8 FTE per week.

Funding available until 31^st March 2030 in the first instance.

*Appointment at Research Associate* level is dependent on having a PhD (or equivalent experience), including those who have submitted but not yet received their PhD. Where a PhD has yet to be, awarded appointment will initially be made at research assistant and amended to research associate when the PhD is awarded (PhD needs to be awarded within 6 months of the start date).   

Location: Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, Worts Causeway, Cambridge, CB6 2WR

Informal enquiries can be made to the CD3 team (cd3@medschl.cam.ac.uk), who will connect you with the appropriate investigators.

Closing date: 3^rd November 2025

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