187 web-developer-university-of-liverpool Postdoctoral positions at University of Oxford in United Kingdom
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collaborators and colleagues from within the University to achieve a range of real-world outcomes. Typical outputs include top-tier scientific publications, patents, and seeing collaborators translate our work
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based at University College London and the Environmental Change Institute at the University of Oxford, with offices in both London and Oxford. The new research group is supported by long-term funding from
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to determine the activators of inflammation in atherosclerosis. You will identify and develop suitable techniques, and apparatus, for the collection and analysis of data (e.g. flow and mass cytometry, confocal
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O’Brien’s research groups at the Department of Engineering Science (Central Oxford). The post is fixed term for two years and is funded by the EPSRC. The development of large-scale quantum computers will
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calculations and Brownian Dynamics simulations. The group is looking for a highly motivated and driven postdoctoral researcher to contribute strongly to a wave of ongoing developments deploying this technology
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on the electrosolvation force under development in the group. The planned investigations are primarily experimental in nature, but will proceed in close conjunction with insight from theory and simulations. The ideal
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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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About the role We have an exciting opportunity to join the dynamic research group led by Dr Jie Yang in the Department of Oncology at the University of Oxford. The group conducts research on T cell
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establish and validate microfluidic co-culture systems using human glomerular cells and benchmark these platforms against human kidney multi-omic and spatial datasets. These systems will be further developed
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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