58 scientific-computing Postdoctoral positions at University of Oxford in United Kingdom
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scientific programming and independently managing a discrete area of a research project are desirable, but not essential. The deadline for applications is midday on September 11 2025. Interviews will be
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computational workflows on a high-performance cluster. You will test hypotheses using data from multiple sources, refining your approach as needed. The role also involves close collaboration with colleagues
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conventional boundaries and pursue hypothesis-led science. We will make every effort to support the successful candidate to research independence through the programme, and with training and mentoring
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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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full-stack approach to suppressing errors in quantum hardware. This research focuses on achieving practical quantum computation by integrating techniques ranging from hardware-level noise suppression
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About the role We are seeking a full-time Postdoctoral Research Assistant to join the Computing Infrastructure research group at the Department of Engineering Science (central Oxford). The post is
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Wellcome Trust and by project-specific funding from the EU Horizon programme. As part of a multi-disciplinary research team, you will contribute to analyses of the global food system. Some of the interests
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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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programme grant with partners across the UK to facilitate the use of hydrogen for aviation, and in particular the icing vulnerability of heat exchangers and parts of the airframe. You will work to generate
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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