184 software-verification-computer-science-"TCAT-Jacksboro" Postdoctoral positions at University of Oxford
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About us: At the Department of Physiology Anatomy & Genetics (DPAG) we undertake discovery science where we reassemble physiological processes at the molecular, cellular, tissue and systems level
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experience. They will possess sufficient specialist knowledge in the discipline to work within the research programme and be able to contribute ideas for new research projects and research income generation
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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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medicine, with a primary focus on optimizing clinical trial design. The partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry
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demonstrable related experience. They will possess sufficient specialist knowledge in the discipline to work within the research programme and be able to contribute ideas for new research projects and research
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months), and is full-time. Applicants will have, or be close to completing a PhD/DPhil in statistics, computer science, or related areas. They will have excellent communication skills, including
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We are seeking a full-time Postdoctoral Research Assistant to join RADlab at the Department of Engineering Science (central Oxford). The post is funded by UKRI and is fixed-term (1st January 2025
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out rigorous and impactful research into the computational mechanisms of human learning using deep neural network models, and disseminating the findings within the research group, across the wider
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new collaborations within the centre. You must hold a PhD (or near completion) in statistical genetics, functional genomics, computational biology, or a related field together with proficiency in
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researchers will extend and apply the ideas of active matter physics in biological contexts, developing theories and cell-scale and continuum computational models. The work will focus on identifying physical