384 parallel-computing-numerical-methods Postdoctoral research jobs in United Kingdom
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The Oxford Department of International Development (ODID) is seeking to recruit a Comparative Data & Methods Postdoctoral Researcher. Reporting to Professor Masooda Bano, the post-holder will join a
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such as breakwaters by using computational tools developed in our research group. These tools are based on discrete element method and they have bee validated against experimental data. You will start your
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Data & Methods Postdoctoral Researcher. Reporting to Professor Masooda Bano, the post-holder will join a research team funded by a European Research Council (ERC) Advanced Grant, Choosing Islamic
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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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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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Location: Dept of Engineering Science, Parks Road, Oxford, OX1 3PJ About the role We are seeking a full-time Postdoctoral Research Assistant to join the Computing Infrastructure research group
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develop and evaluate computational methods for modelling patient-specific aortic and valvular haemodynamics. The position is funded until March 31st 2028 in the first instance, and available immediately
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develop and evaluate computational methods for modelling patient-specific aortic and valvular haemodynamics. The position is funded until March 31st 2028 in the first instance, and available immediately
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About us The researcher will join the Human-Centred Computing (HCC) group at King’s at the Department of Informatics, which provides a supportive and collaborative environment and an exciting mix
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architecture of entirely new foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across