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for candidates interested in acquiring experience of research in a world-class academic unit (see http://www.wrh.ox.ac.uk). The post supports a Phase III programme of work aimed at testing and validating
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researchers in the Future of Food programme at the Oxford Martin School. You must hold or be close to the completion of a doctoral degree in a relevant field (e.g. data science, industrial ecology, geography
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Colorectal Cancer - Stratification of Therapies through Adaptive Responses (CRC-STARS) programme, developing and applying cutting-edge mathematical methods to spatial transcriptomics imaging data in order to
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/workforce-and-learning-research-group ). The Compound Pressures project is a realist review funded by the NIHR HSDR programme. The project aims to understand how multiple and intersecting pressures, such as
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Applications are invited for a Postdoctoral Researcher in Health Modelling, to work with a team working with Dr Ben Amies-Cull on a research programme on the Cities for Better Health: Child Obesity
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these bioinformatic experiments. Access to a high-performance computer will be provided. The candidate must be capable of generating complex molecular compound models in silico and using current molecular dynamic
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of agentic behaviour and publishing high-impact research. Candidates should possess a PhD (or be near completion) in PhD in Computer Science, AI, Security, or a related field. You will have a Strong background
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to completion of a PhD or equivalent qualification in computational fluid dynamics or applied mathematics. What we offer At the university of Oxford your happiness and wellbeing at work is important to us. We
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The Oxford Internet Institute has an exciting opportunity to join the Governance of Emerging Technologies research programme, working under the supervision of Professor Brent Mittelstadt and
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Oxford’s Department of Orthopaedics (NDORMS) as well as collaborators in Bristol and Cardiff. You should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely