224 cloud-computing-"https:"-"https:"-"https:"-"https:"-"St" Postdoctoral positions in United Kingdom
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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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Postdoctoral Researcher. The group aims to identify, understand, and develop therapies for rare genetic disorders. The group is primarily computational but partners with multiple international labs (including
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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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a project investigating prehabilitation for cancer populations in collaboration with the Cancer Treatment Outcome Programme at the Country Durham and Darlington NHS Trust. Prehabilitation means
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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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Programme. • Our on-site nursery is rated Outstanding by Ofsted, and you can access holiday camps for children aged 5-16. • Our family-friendly policies, including maternity and adoption leave, are
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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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About the Institute for Advanced Studies, New Uzbekistan University The Institute for Advanced Studies (IAS) at New Uzbekistan University is a leading research center dedicated to cutting-edge scientific discoveries and technological innovation. As a hub for interdisciplinary research, our...
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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