We are seeking an enthusiastic Postdoctoral Research Assistant in Medical Statistics, with an interest in Artificial Intelligence (AI), to join the Computational Health Informatics (CHI) Laboratory, in the Institute of Biomedical Engineering in the Department of Engineering Science (Headington, Oxford). The CHI Lab is led by Prof David Clifton, the Royal Academy of Engineering Chair of Clinical Machine Learning, within the Department of Engineering Science

The full-time post is funded by the Innovation and Technology Commission and is fixed-term for up to 12 months, with the possibility of an extension. The CHI Lab is one of the leading groups for AI in Healthcare, and one of the largest groups in the Department of Engineering Science, with a friendly, close-knit collaborative team that undertakes impactful machine learning expertise with world-leading clinicians across Oxford’s Medical Sciences Division.

This post is part of collaborations with senior colleagues in the Medical Sciences Division and the Oxford University Hospitals NHS Foundation Trust. You will be responsible for working with senior medical statisticians to develop medical statistical methods for improving our understanding of patient physiological conditions, across a number of high-profile collaborations. This will be based on developing tools for analysing very large electronic health record (EHR) and sensor datasets from the Oxford University Hospitals NHS Foundation Trust and other clinical sources. There is substantial opportunity for flexibility on topic according to the interests and expertise of the post holder, as part of a vibrant and successful team, where career development for scientists is a high priority within one of the world’s fastest-growing areas for AI.

You should have a PhD (or be close to completion) in mathematics, medical statistics, computational statistics, or cognate discipline. You should also have experience of working in a highly interdisciplinary team, a good knowledge of medical statistics, with a good publication record in the scientific literature.





Only online applications received before midday on5 April 2024can be considered. You will be required to upload a covering letter/supporting statement, including a brief statement of research interests (describing how past experience and future plans fit with the advertised position), CV and the details of two referees as part of your online application.

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