Research Fellow In Multimodal AI for Cancer Risk Prediction

We are seeking a highly motivated and skilled Research Fellow to join the Cancer Data-Driven Detection (CD3) programme. 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)."

The post-holder will play a central part in CD3's effort to develop next-generation cancer risk models. Working within the advanced analytics workstream, they will bring together electronic health records, administrative datasets, and emerging multi-omic measurements to build and test machine-learning-based multimodal risk prediction tools. A key element of the role will be to explore how different data types and AI-derived features can contribute to risk, identify robust and clinically meaningful signals, and work closely with collaborators across the programme to ensure models are transparent, reliable and suitable for real-world use. The position offers the opportunity to shape core analytical methods used across CD3 and to contribute to one of the largest coordinated efforts in the UK to transform early cancer detection through data.

The post will be based in the School of Biological Sciences in Southampton but will involve co-mentoring and close collaboration with investigators across multiple institutions, reflecting the highly collaborative nature of the programme. Based within the Data-driven Biology Group, the postholder will develop and validate AI and data science tools for multi-cancer risk prediction models using population-scale, multimodal datasets, including electronic health records, administrative data, and multi-omic data.

About You
You will have a solid quantitative background and an interest in applying data-driven and AI methods to questions that matter for public health. We are looking for someone who can work confidently with large and varied health and omic datasets, and who enjoys developing and testing new analytical approaches. Experience in machine learning and combining different data types will be critical to the role, but we are just as interested in your ability to think clearly, learn new methods, and work well with others. CD3 is a highly collaborative programme, and the post calls for someone who is comfortable engaging with researchers from different disciplines and institutions.

As well as additional benefits that will make your life easier, such as a generous holiday allowance and additional university closure days. We will help you find a good work-life balance with flexible, or even part-time, working hours. We will support your long-term future too, with access to the Universities Superannuation Scheme (USS)*, subsidised health and fitness facilities and a range of discounts.

This post is offered on a three-year. Part-time appointments (up to 80%) will be considered on a pro-rata basis.

For additional information or informal enquiries before submitting your application, please contact Prof. Owen Rackham )

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Further Details

• Job Description and Person Specification

As a university we aim to create an environment where everyone can thrive and are proactive in fostering a culture of inclusion, respect and equality of opportunity. We believe that we can only truly meet our objectives if we are reflective of society, so we are passionate about creating a working environment in which you are free to bring your whole self to work. With a generous holiday allowance as well as additional university closure days we are committed to supporting our staff and students and open to a flexible working approach.

Apply by 11.59 pm GMT on the closing date. For assistance contact Recruitment on or quoting the job number.

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