Postdoctoral Researcher in Artificial Intelligence for Robust Clinical Prediction Modelling

Location: Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Oxford OX3 7LF 

We invite applications for a Postdoctoral Researcher to join the research group of Professor Christopher Yau (http://cwcyau.github.io) at the Big Data Institute, University of Oxford. This post will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. 

Funded by the Medical Research Council (MRC) and the National Institute for Health and Care Research (NIHR), the project aims to advance artificial intelligence (AI) methods that improve the reliability of clinical prediction models when faced with data drift, bias, and fairness challenges. The research will involve developing deep learning and synthetic data generation approaches and applying them to exemplar studies in ovarian cancer prognosis, COVID-19 prediction, and childhood mental health. 

You will have (or be close to completing) a PhD in a quantitative discipline such as computer science, mathematics, statistics, engineering, or a related field. Strong programming skills and experience in machine learning or statistical modelling are essential. Experience with healthcare data, algorithmic fairness, or deep learning for biomedical data will be advantageous. The successful candidate will contribute to high-impact publications, open-source software development, and training materials. 

This position is full-time and fixed-term for 30 months. This post is available from January 2026 and must end by 30 November 2028.  

Applications for flexible working arrangements are welcomed and will be considered in line with business needs. 

You will be required to upload a CV and Supporting Statement as part of your online application. Click here for information and advice on writing an effective Supporting Statement: https://www.jobs.ox.ac.uk/cv-and-supporting-statement.

The closing date for applications is 12.00 noon on Monday 24th November 2025. Interviews are expected to take place on Wednesday 10th December 2025.

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