Research Fellow

Are you a statistician or machine learning researcher interested in improving the healthcare system? Do you have experience with prediction modelling and large datasets? Do you want to push the boundaries of what is possible with NHS data, and help to directly improve people’s health?

The PREDICT project (Pragmatic Recalibration and Evaluation of Drift In Clinical Tools) aims to drive major change to the regulation of prediction models within the healthcare domain, and is funded by the National Institute for Health Research. Our team from the University of Leeds will work directly with stakeholders including NHS England, TPP, EMIS, and the MHRA to discuss how to resolve the issue of temporal drift in the UK healthcare system. 

PREDICT is focussing on the issue of prediction models becoming less accurate over time as underlying changes to demographics and the healthcare system are reflected in electronic healthcare records. We will be implementing open-source software implementing three major approaches to detecting (and potentially resolving) temporal drift issues in models such as QRISK and eFalls - aiming to deploy this software into the OpenSAFELY platform. We will also be interacting with regulatory bodies and patient groups, to drive regulatory change in this area and improve the standard of healthcare within the UK. 

The University of Leeds team comprises of Assoc. Prof. Samuel Relton, Snr. Fellow Kate Best, and Assoc. Prof. Oliver Todd. As a Research Fellow in Health Data Science, you will take responsibility for implementing algorithms for detecting and repairing temporal drift including regular audits, statistical process control, and Bayesian regression. You will also be involved in designing and running our Patient and Public Involvement (PPI) workshops, aiming to garner public opinion and produce a short video to communicate their opinion on this topic. The ideal candidate will have the ability to work well both individually and in a team, and a strong commitment to your own continuous professional development. 

Holding a PhD (or close to completion) in Computing, Mathematics, or a closely allied discipline, or have equivalent experience, you will have programming skills and evidence of the ability to develop and evaluate prediction models. Some travel to UK study sites will be required. 

What we offer in return

26 days holiday plus approx.16 Bank Holidays/days that the University is closed by custom (including Christmas) – That’s 42 days a year!

Generous pension scheme plus life assurance– the University contributes 14.5% of salary

Health and Wellbeing: Discounted staff membership options at The Edge, our state-of-the-art Campus gym, with a pool, sauna, climbing wall, cycle circuit, and sports halls.

Personal Development: Access to courses run by our Organisational Development & Professional Learning team.

Access to on-site childcare, shopping discounts and travel schemes are also available.

And much more!   

Information for international candidates

Please note that this post may be suitable for sponsorship under the Skilled Worker visa route but first-time applicants might need to qualify for salary concessions. For more information please visit: www.gov.uk/skilled-worker-visa;

For research and academic posts, we will consider eligibility under the Global Talent visa. For more information please visit: www.gov.uk/global-talent 

To explore the post further or for any queries you may have, please contact:

Samuel Relton, Associate Professor in Health Data Science 

Email: S.D.Relton@leeds.ac.uk 

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