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We are seeking an ICT based Technical Delivery Manager, working as part of Research Computing Services at Imperial to help us deliver the forthcoming Research Excellence Framework 2029 (REF 2029
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: Applicants should have, or expect to achieve, at least a 2:1 honours degree (or equivalent) in a relevant subject, typically in the physical sciences or engineering. Experience of computer simulation, or
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core member of the King’s Clinical Academic Training Office (KCATO); this role in particular will have overall programme management of the fellowship schemes funded by the King’s Health Partners Centre
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About the role As a Lecturer in Computer Science, you will contribute to the delivery of undergraduate and postgraduate teaching. You will support students through lectures, tutorials and project
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manufacturing sectors, from SMEs to large global manufacturers. For details, visit the MTC website . Entry requirements: A 1st or high 2:1 degree in computer science, manufacturing/industrial engineering, data
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our Next Generation Infrastructure (NGI) Programme Board with significant experience in managing change involving major capital investment and associated business transformation. Experience serving
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ultraviolet light. Our scientists use the light to study a vast range of subject matter, from new medicines and treatments for disease to innovative engineering and cutting-edge technology. Now is an exciting
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LSE is committed to building a diverse, equitable and truly inclusive university Salary from £36,513 to £41,565 pa inclusive with potential to progress to £44,468 pa inclusive of London allowance The Department of Social Policy at the London School of Economics and Political Science is a...
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As a University of Applied Learning, Singapore Institute of Technology (SIT) works closely with industry partners to deliver impactful, application-driven research. This position supports a
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Engineering, Computer Science, Data Science, Statistics, or equivalent. Strong theoretical background in statistics and machine learning. Knowledge of the basics of federated learning and causal inference is