Research Associate* in Cancer Risk Modelling (Fixed Term)

University of Cambridge; Cambridge; Hybrid; GBP 37,000 - 47,000; Full time

Job summary

A leading UK university is seeking a motivated Research Associate to develop and validate multi-cancer risk prediction models. Focused on improving cancer risk prediction using diverse datasets, the role requires a PhD (or near completion) and expertise in statistical modelling and machine learning. The position supports hybrid working and encourages applications from diverse backgrounds.

Qualifications

• PhD (or near completion) in relevant fields.
• Expertise in statistical modelling and machine learning.
• Experience with complex health data.

Responsibilities

• Develop and apply advanced epidemiological approaches.
• Create multi-cancer risk prediction models.
• Evaluate model performance across datasets.

Skills

Statistical modelling

Machine learning

R

Python

Communication skills

Collaboration

Education

PhD in epidemiology, biostatistics, or related discipline

Job description

Overview

Research Associate* in Cancer Risk Modelling

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.

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.

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.
• Contribute to developing new methodology where best practice is unclear and evaluate model performance and transferability across diverse datasets and populations.

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.

Desirable experience

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 31st 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 ([e-mail]), who will connect you with the appropriate investigators

As a group, we value and encourage applications from a diversity of background and experience to contribute to the highly interdisciplinary research programme. We strongly value and encourage Equity, Diversity and Inclusion as well as a flexible working environment.

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).

Application process

Please ensure that you upload a covering letter and CV in the Upload section of the online application. The covering letter should outline how you match the criteria for the post and why you are applying for this role. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.

Please include details of your referees, including email address and phone number, one of which must be your most recent line manager.

Closing date: 3rd November 2025

Interview date: 19th November

For information about how your personal data is used as an applicant, please see the section on Applicant Data on our HR web pages.

Please quote reference RS47410 on 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.