115 computer-programmer-"FEMTO-ST"-"FEMTO-ST" Postdoctoral positions at University of Oxford
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/test articles), intrusive probes, and optical diagnostics. You’ll plan and run test campaigns, analyse data to advance understanding of material–flow interaction, and disseminate results in seminars
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collaborative links thorough our collaborative network. The researcher should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely related field. You have an
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in tackling many global challenges, from reducing our carbon emissions to developing vaccines during a pandemic. The Department of Computer Science at Oxford is renowned for pioneering research and
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Applications are invited for a Postdoctoral Research Assciate(s) in the theory of quantum systems. This post is for 2 years. This project will explore theory of quantum computing and simulation
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record in a related field, and possess sufficient specialist knowledge in the discipline to work within established research programmes. You will have the ability to identify research objectives and to
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proven expertise in seismic data processing and analysis, knowledge of volcanic/ geothermal processes, strong quantitative skills, and proficiency in Python for scientific computing. You should be
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Postdoctoral Researcher. The group aims to identify, understand, and develop therapies for rare genetic disorders. The group is primarily computational but partners with multiple international labs (including
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development (ECD) and raise global visibility of climate impacts on ECD. The post holder will be a member of Climate Research Programme at ECI in SoGE, reporting to Dr Neven Fučkar, Senior Researcher, and there
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. The provision of scholarships for postgraduate students is an area of crucial importance for the University. Student Fees and Funding (SFF)’s scholarships team assists in this by managing scholarship programmes
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly