115 parallel-computing-numerical-methods Postdoctoral positions at University of Oxford
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We are seeking a highly motivated Postdoctoral Research Scientist with a strong background in human induced pluripotent stem cell (iPSC) differentiations and computational analysis to join Dr
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mechanism design. The project will involve close collaboration with project teams at Imperial College London, the Edinburgh Parallel Computing Centre (EPCC) and the Luxembourg Institute of Science and
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focus on ambitious, ‘blue sky’ research for novel methods development relevant for drug discovery analysis pipelines, trial design and operational efficiency. Led by Professor Chris Holmes, and with
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods
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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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multiple aspects of work to meet deadlines. You will adapt existing and develop new scientific techniques and experimental methods, as well as contribute ideas for new research projects. You will use
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computational sciences, decision-maker education campaigns, and training the next generations of technology governance leaders. It is one of the few organisations in the world to focus on the governance of AI
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and clinical neuroscience. This project involves development of machine learning methods for mapping the relationships between diffusion MRI (dMRI) and phase-sensitive OCT (PS-OCT) in the same tissue
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the model, its numerical implementation in a finite element code, and its validation against experimental data in collaboration with experimental collaborators. You will also engage with the hub activities
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Computational Methods for Advanced Research to Transform Biomedicine ( SMARTbiomed ), an international collaboration that integrates large-scale, multimodal biomedical data with advances in statistical and