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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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Metabolism (OCDEM) on studies related to circadian rhythms in population health. This post is part of a large, interdisciplinary research programme, offering attractive opportunities to work across
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emphasis on research infrastructure and technology rather than preparation for an academic career path. You will be involved in research, but more focused on learning and improving how computing, workflows
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numerical modelling. Ability to participate in research meetings to troubleshoot research problems and discuss the direction of research. Skills Fluency in at least one scientific computer language (e.g., C
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interdisciplinary research programme involves national and international collaborators. We are a vibrant, enthusiastic, innovative and strategic enterprise with an internationally leading position to make a real
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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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). Outstanding organisational and record-keeping skills and a capacity to work independently, once appropriate training and guidance is provided. Excellent academic writing, oral presentation skills and computer
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explores novel aggregation methods at the intersection of AI safety, computational social choice, and judgment aggregation, aiming to formally integrate multi-stakeholder preferences into AI system design
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neurobiology or a strong quantitative preparation with experience in bioinformatics/computer science will be essential. Prior experience with connectomics data is highly desirable. Our group has developed
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-generation AI hardware (ASIC) accelerators. The UK's Advanced Research Invention Agency (ARIA) is supporting an ambitious programme of work that aims to reduce the the cost of AI by more than 1000x: https