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to circadian rhythms in population health. This post is part of a large, interdisciplinary research programme, offering attractive opportunities to work across conventional boundaries and pursue hypothesis-led
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of Research Excellence and contribute to a growing cardio-immunology research programme. Inflammation is increasingly recognised as a key modifiable driver of adverse outcomes across a range of cardiovascular
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, and building up an independent research profile you publish internationally and present papers in conferences and workshops give you independently teach courses in the BA program (proseminars), one
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excellent computational skills. The post is attached to Prof. Eric French's Montague Burton Professorship. The research aims at exploring and understanding the drivers of medical spending, and how medical
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anxiety, to work within the established research programme. Substantial hands-on research and professional experience of working with individuals with mental health difficulties, including first-hand
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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research initiative funded by ARIA, titled Aggregating Safety Preferences for AI Systems: A Social Choice Approach. The project operates at the interface of AI safety and computational social choice, and
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the starting date is negotiable. The current funding is guaranteed from the Montague Burton Fund. We are looking for a candidate who is a health economist with excellent computational skills. The post is
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qualification/experience equivalent to PhD level in a relevant subject area (physics, engineering, computing science, etc.). You will need as essential skills a good knowledge of C and python, familiarity with
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qualification/experience equivalent to PhD level in a relevant subject area (physics, engineering, computing science, etc.). You will need as essential skills a good knowledge of C++ and python, familiarity with