454 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "ETH Zürich" Postdoctoral research jobs in United Kingdom
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and should also clearly describe how you meet each of the selection criteria listed in the job description. Click here for information and advice on writing an effective Supporting Statement. To discuss
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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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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