310 computer-programmer-"https:"-"Prof" "https:" "https:" "https:" "https:" "CNRS" Postdoctoral research jobs in United Kingdom
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- VIN UNIVERSITY
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Hopkins University (USA). About the role The PDRA will lead the development of new computational and mathematical models to quantify and predict infectious disease risk, particularly for identifying high
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qualification/experience in a related field of study and be able to demonstrate sufficient knowledge in the discipline and of research methods and techniques to work within established research programmes
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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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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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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