35 parallel-computing-numerical-methods Postdoctoral research jobs at University of Oxford;
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statistical and computational methods designed to use “big data” and to address questions of direct or indirect relevance to common complex diseases and disorders. The appointee will join the group of Professor
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About the role We are seeking a full-time Postdoctoral Research Assistant to join the Numerical Analysis research group at the Department of Engineering Science (Osney). The post is funded by Rolls
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Oxford – a friendly, welcoming place of work with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC
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the application of advanced computational techniques spanning a range of temporal and spatial scales to address some important and fundamental questions regarding the nature of how trafficking is controlled within
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We are seeking a full-time Postdoctoral Research Associate in Computational Mechanics for Solid State Batteries. The post holder will be based at the Department of Engineering Science (central
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, engineering and AI with industry scientists, forming small teams focused on ambitious, ‘blue sky’ research for novel methods development relevant for drug discovery analysis pipelines, trial design and
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, engineering and AI with industry scientists, forming small teams focused on ambitious, ‘blue sky’ research for novel methods development relevant for drug discovery analysis pipelines, trial design and
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for this post. The successful candidate will be required to develop a personal research programme in theoretical cosmology (which may include numerical modelling and/or data analysis), interacting with faculty
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scientists, forming small teams focused on ambitious, ‘blue sky’ research for novel methods development relevant for drug discovery analysis pipelines, trial design and operational efficiency. Led by Professor
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to advance artificial intelligence (AI) methods that improve the reliability of clinical prediction models when faced with data drift, bias, and fairness challenges. The research will involve developing deep