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will be jointly supervised by: Dr Dominik Leichtle, School of Informatics, University of Edinburgh Dr Elham Kashefi, School of Informatics, University of Edinburgh Dr Theodoros Kapourniotis, National
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Scientific Researcher Salary £40,832 per annum Fixed term, 12 months MRC Laboratory of Molecular Biology, Cambridge, UK An opportunity is available for a Scientific Researcher to join the group
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This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research
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co-design workshops with men from Black backgrounds and lower income backgrounds, in communities across Yorkshire. You will lead data analysis within the team and help develop targeted recruitment
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with a background in cognitive psychology, data science or computer science and a willingness to develop skills in computational models of cognitive processes, statistical methods, and programming (R
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and/or Python, is highly desirable. Experience of collecting neuroimaging and/or wearables data from infants/children is also desirable. The project will be based at the Institute for the Science of
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but not essential. A strong background in materials science and/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition
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of collecting neuroimaging and/or wearables data from infants/children is also desirable. The project will be based at the Institute for the Science of Early Years (www.isey.org ) in London, UK. Supervision will
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sits at the cutting edge of materials science and solid mechanics, using real-world microstructure data to simulate thermo-mechanical behaviour at the grain scale. Your work will form a key part of a
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
generate vast amounts of operational and maintenance data, much of it remains fragmented and underutilized. Unlocking insights from this unstructured data could enable earlier fault detection, improved