12 Computer-Science-"https:" "https:" "https:" "https:" "https:" PhD research jobs in United Kingdom
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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underrepresented groups. For more information, please see our website https://www.hw.ac.uk/uk/services/equality diversity.htm and also our award-winning work in Disability Inclusive Science Careers https
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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September 2026. Find out more about the Faculty of Medical Scienceshere: https://www.ncl.ac.uk/medical-sciences/ Find out more about our Research Institutes: https://www.ncl.ac.uk/medical-sciences/research
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for Skilled Worker sponsorship under the UK Visa & Immigration points-based system. Information on alternative visa options is available on www.gov.uk. Our website https://www.sheffield.ac.uk/eee For informal
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, relating to craniofacial identification research and machine learning. You will require a computer science background. You will be applying AI and/or machine learning to Face Lab processes in relation
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, relating to craniofacial identification research and 3D digital avatars. You will require a 3D animation, CGI, computer science and/or anatomical modelling background. A knowledge of anatomy and 3D
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. You will contribute to the UKRI-supported Centre for Heterogeneous Integrated Micro Electronic and Semiconductor Systems (CHIMES²) programme. CHIMES² is funded by the Department for Science, Innovation
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laboratory Publish and present your results at leading international venues, and complete your doctoral thesis in the Aalto Doctoral Programme in Electrical Engineering Contribute to teaching duties as part of
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work closely with an interdisciplinary team spanning microbiology, engineering, and computation, and will contribute to developing predictive models that link bacterial physiology to infection outcome