54 scientific-computing Postdoctoral research jobs at University of Oxford in United Kingdom
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for data analysis/scientific computing, and excellent decision-making, problem-solving, planning, and organisational skills. Please direct enquiries about the role to Prof Martin Bureau: (martin.bureau
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Science Park. The post is funded by Innovate UK and is fixed-term to 30th April 2026. The CEBD project is an ambitious programme to develop the first category enhanced battery powered eVTOL. The project
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inference attacks, to mitigate privacy leaks in MMFM. You will hold a PhD/DPhil (or be near completion) in a relevant discipline such as computer science, data science, statistics or mathematics; expertise in
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the performance of lithium ion technologies. To support the programme, the post holder will be required to carry out research on characterisation of battery degradation, with a particular focus on the application
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codes running on high-performance computer clusters, experience of presenting scientific results in peer-reviewed journals or delivering papers at international conferences, excellent communication skills
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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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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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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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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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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