254 computer-programmer-"https:"-"CNR"-"https:"-"https:"-"https:"-"UCL" Postdoctoral positions in United Kingdom
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and national industries, the school hosts several externally funded research programmes in key research themes including healthcare and tissue engineering. This 48-month Research Fellow position in
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funded by the Leverhulme will collaborate with the PDRA on parts of the project. The candidate should be able to provide evidence of expertise in visual psychophysics and should have strong computing
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of the atmosphere for research purposes. An excellent knowledge of atmospheric physics. A strong experience in a scientific computing and data visualisation language. The ability to work independently and as part of
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University (USA), and Google Deepmind (London). About the role The PDRA will lead the development of new computational and mathematical models to quantify and predict infectious disease risk, particularly for
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, psychology, computational neuroscience, biomedical engineering, or a closely related quantitative STEM discipline. A strong background in cognitive, behavioural, and/or systems neuroscience, with relevance
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archaea, many of which also use histones as major chromatin building blocks, and non-model bacteria. In the lab we combine a variety of computational and experimental techniques, including phylogenomics
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part of an interdisciplinary team working across physical and computer science to develop new pathways to the design and discovery of inorganic materials as part of the Leverhulme Centre for Functional
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applicant will contribute to the development of novel mathematical and computational methods for image analysis and AI, with a particular emphasis on interpretable and uncertainty-aware approaches motivated
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sensing. are essential as well as strong computing skills, including the knowledge of UNIX/Linux, Fortran, Python, or other high-level languages. The post is full time and fixed term for 18 months
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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