100 parallel-and-distributed-computing Postdoctoral research jobs at University of Oxford
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atmospheric plasmas are inaccurate and do not produce the correct compositions and energy distributions of expanding flows. The proposed research aims to make a step change in domestic and international
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ovarian cancer prognosis, COVID-19 prediction, and childhood mental health. You will have (or be close to completing) a PhD in a quantitative discipline such as computer science, mathematics, statistics
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execute experiments and contribute conceptually to the overall research programme. The post-holder should hold, or be close to completion of, a PhD/DPhil in biochemistry, molecular/cell biology or genetics
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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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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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multidisciplinary team approach to cardiovascular research. The research programme will involve collaboration with researchers from UK-wide centres under the leadership of Oxford and Leeds. We are looking for a
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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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the universities of Manchester and Oxford. The post-holder will be one of six centre-funded postdoctoral researchers delivering on projects that form our core research programme. They will be a cornerstone of the
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contributory pension scheme 38 days annual leave A comprehensive range of childcare services Family leave schemes Cycle and electric car loan schemes Employee Assistance Programme Membership to a variety of
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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