123 postdoc-computational-fluid-dynamics Postdoctoral positions at University of Oxford
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audiences and an ability to work well independently using own initiative, as well as part of a team. Experience in a variety of advanced microscopy techniques and computational biology expertise are desired
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project will involve both remote-sensing and field-based observations and data collection. It will provide outputs to the World Bank CAWEP (Central Asia Water Energy Power) programme to aid the design
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leading academic and industry experts, and an active academic/industry exchange program that aims to accelerate career development for the postholder employed on the project. About you The postholder should
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Medicine, University of Oxford is to re-programme immune cells as part of a larger programme to develop novel therapeutics (viral vector and/or extracellular vesicle-based) for myocardial regeneration. Key
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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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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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posters locally and at national and international conferences, as well as participate in MRC grant’s research programme led by Prof Marco Fritzsche. You must hold a relevant PhD/DPhil (or be near completion
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engineering, computer science or other field relevant to the proposed area of research. You should have a good track record of robotic publications/presentations in the field of healthcare, possess sufficient
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, operations research, computer science, mathematical finance, or a related field, the successful candidate will demonstrate the ability to develop independent research ideas and contribute to advancing our
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associated computing code for modelling avian influenza outbreaks in Great Britain (GB). One position will focus on modelling the risk of virus invasions into GB in different locations and at different times