282 data-"https:"-"https:"-"https:"-"https:"-"https:"-"Stanford-University" Postdoctoral positions in United Kingdom
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on delivering high-quality data and mathematical models to inform the response to emerging veterinary parasitic disease threats and the impacts of global change on disease risk worldwide. The research group sits
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information about working at the Department, see www.eng.ox.ac.uk/about/work-with-us/ Only online applications received before midday on Friday 03 April 2026 can be considered. You will be required to upload a
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reports and grant proposals. You should possess a PhD or DPhil (or near completion of) in Machine Learning or Maths. Informal enquiries may be addressed to jakob@robots.ox.ac.uk For more information about
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information on the Scott group and our research can be found here . As a member of Research Staff at the University of Bath, you will be encouraged to take up a minimum of 10 days professional development pro
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, to deliver the final stages of this multi‑year research programme and support the production of high‑quality scientific outputs. About you- You will bring strong data analysis skills, including
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will be based at the Institute for Medical Humanities, working closely with Dr Arya Thampuran (Narrative Practices Lab). They will be expected to lead data collection, manage the day-to-day running of
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collaborators at DU ES and the industrial partner WLL. Experience conducting quantitative research including fracture data collection, management, and analysis is essential. Excellent initiative, flexibility in
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data science techniques to model and classify population health risk and disease dynamics at multiple spatial and temporal scales. The PDRA will lead the development of novel approaches for managing
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for real-time human data processing in interactive settings. Technical expertise in areas such as electrophysiological recording, VR paradigm design, closed-loop algorithm development, or clinical
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of reproducing aerodynamic loads in real time for physical experiments. The project combines numerical modelling, data-driven methods, and laboratory experimentation to advance next-generation real-time hybrid