Overview
Research Associate* in Cancer Risk Modelling (Fixed Term) – Minderoo Centre for Technology and Democracy. We are seeking a highly motivated Research Associate to join the Cancer Data-Driven Detection (CD3) programme. CD3 is a national, multi-institutional research initiative focused on using data to advance understanding of cancer risk and enable early interception of cancers. The post is based at the Centre for Cancer Genetic Epidemiology (CCGE) in Cambridge with co-mentoring and collaboration across multiple institutions. The role is within the Multi-Cancer Risk Prediction Driver Programme, developing and validating novel multi-cancer risk prediction models using population-scale, multimodal datasets (electronic health records, administrative data, and multi-omic data).
Responsibilities
* Develop and apply advanced epidemiological, statistical, and AI-based approaches to improve prediction of cancer risk across multiple tumour types.
* Develop data domain-specific multi-cancer risk prediction models.
* Integrate individual multifactorial cancer models into robust, equitable, and generalisable multi-cancer prediction tools.
* Evaluate model performance and transferability across diverse datasets and populations; contribute to methodology development where best practice is unclear.
Qualifications
* 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 in risk prediction modelling (including survival analysis, competing risks, or multivariate outcomes) and working with population-scale health data (e.g., electronic health records, cohort studies, or multi-omic datasets).
Additional Information
The CCGE and Department support hybrid working; staff are expected to work onsite regularly to foster collaboration and community.
This is a full-time position. We welcome applications from those wishing to work part-time of no less than 0.8 FTE per week.
Funding available until 31 March 2030 in the first instance. 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.
We value diversity and equity; we encourage applications from diverse backgrounds and support Equity, Diversity and Inclusion as well as a flexible working environment.
How to apply: Click the Apply button to register an account and apply online. Please upload a covering letter and CV in the Upload section, outlining how you meet the criteria and why you are applying. Include details of referees (one must be your most recent line manager).
Closing date: 3 November 2025. Interview date: 19 November 2025. For information about how your personal data is used, please see Applicant Data.
Please quote reference RS47410 on your application and in any correspondence. 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.
Seniority level
* Entry level
Employment type
* Full-time
Job function
* Research, Analyst, and Information Technology
* Industries
* Research
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