280 computer-security "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "St" positions at University of Nottingham
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The University Of Nottingham Sport is currently undergoing an ambitious change and investment programme to further support our vision to deliver an outstanding student sporting offer and establish
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We are looking for an outstanding PhD student with either strong background in computational modelling or significant experience of laboratory work, who is keen to work at the interface between
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). Record school placement, events and contact details in our localised database. Work closely with academic colleagues to secure school placements and have close communication with schools, mentors and
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of Sport, Exercise, and Nutrition Education – kimberley.edwards@nottingham.ac.uk This project is not funded, and we are seeking a student who can self-fund the PhD. Programme description: Athletes, coaches
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to cover living costs; Join a multidisciplinary cohort to benefit from peer-to-peer learning and transferable skills development. Learn more about the programme, available projects, and the application
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the preparation of teaching specimens, operation and maintenance of apparatus and machinery, stock management, and ensuring compliance with Health and Safety regulations. This includes the safe disposal of clinical
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unique opportunity to work on advanced image analysis and image-driven modelling as part of a wider multi-disciplinary programme that includes mathematical modelling, cancer metabolomics and novel
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(School of Medicine), Teaching Associate – thomas.bestwick-stevenson@nottingham.ac.uk This project is not funded, and we are seeking a student who can self-fund the PhD. Programme description: The overall
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they may need to further develop (and for which we will provide support). The applicant will also be expected to work side-by-side with staff recruited in computer science and the wider academic team
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The rapid growth of deep learning has come at an extraordinary environmental and computational cost, yet the standard training paradigm remains remarkably unchanged. Every sample is passed through