51 affective-computing-"https:"-"https:"-"https:" PhD positions at University of Nottingham
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cannot fully explain the interaction between sand and structural materials as they shear and cause abrasion. This lack of understanding affects all granular problems, and so the applicant does not
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Centre of Excellence. This is a 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
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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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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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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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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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we are looking for The candidate should have a 1st or high 2:1 degree in mechanical/aerospace/manufacturing engineering, computer science, physics, mathematics, or related scientific disciplines
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-impact publications, conference dissemination, and the development of transferable skills aligned with both academic and industrial research environments. The work aligns strongly with EPSRC strategic
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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
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(particularly cognitive or applied psychology) Cognitive Science Human–Computer Interaction Engineering or Computer Science Health sciences Experience in empirical research, experimental design, data analysis