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of agentic behaviour and publishing high-impact research. Candidates should possess a PhD (or be near completion) in PhD in Computer Science, AI, Security, or a related field. You will have a Strong background
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(Department of Chemical Engineering, Waterloo Institute for Nanotechnology). Program Details: UM6P will be the administrative body for the program, where candidates will apply to the UM6P Postdoctoral Program
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that analyses the interactions of science, technology, and society. The department offers the English language master program 'Science, Technology, Society', as well as a number of course formats for
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) programme . The above full-time post is available immediately within the Department of Health and Community Sciences, University of Exeter Medical School. This is a fixed-term position until March 2026. About
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, and the US in both engineering and atmospheric science Access to a state-of-the-art, high performance computing cluster You would work as part of a dynamic team working to enable truly sustainable
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engineering, computer science or other field relevant to the proposed area of research. You should have a good track record of robotic publications/presentations in the field of healthcare, possess sufficient
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successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD in developmental cognitive computational science, neuroscience, computer science
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Oxford University Weighting of £1,500 per year (pro rata). Are you passionate about taking a lead role in a cutting-edge project at the intersection of genomics, computational biology, and haematological
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engineering. We aim to unravel the logic of genome organisation and metabolic control—with the bold vision of building synthetic life. In this role, you will develop and apply computational methods to analyse
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supervisor), working with Dr David Watson (Department of Informatics). This position is funded by King’s Climate and Sustainability. The key responsibilities of the role are to develop machine learning