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flooding events and recreation in freshwater. This project is part of an exciting new Doctoral Training Programme in Microbial Genomics for Health Protection in collaboration with the UK Health Security
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health genomics, combining academic research with front-line public health intervention experience. This project is part of an exciting new Doctoral Training Programme in Microbial Genomics for Health
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, providing access to the full programme of research and knowledge exchange and all the opportunities that this affords. The centre was launched in November 2021, funded by a £10 million award from the ESRC
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a closely related field. A strong background in quantum mechanics, solid-state physics, and computational modeling. Previous experience with density functional theory or many-body physics (beneficial
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. The research will combine computational modelling, experimental validation, and machine learning techniques to develop a predictive phenomenological PAC model. The successful applicant will develop and apply
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with programming (Python, MATLAB), background in aerospace, computer science, robotics, or electrical engineering graduates, hands on skills in implementation of fusion/learning based techniques in
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, PhD students, clinicians and computer scientists who will all support the studentship. The successful student will be further supported by members of the wider 4th floor research groups and existing
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motivated applicant to undertake an original and applied programme of research linked to ongoing work led nationally by the Chartered Institute for the Management of Sport and Physical Activity (CIMSPA
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or sensor arrays. Experience generating, processing and analysing large material property datasets including correlating between multiple techniques, or developing computational reconstruction techniques
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methods in the past. A piece of comprehensive computer software, Pythia with the corresponding capabilities have been developed and tested successfully in several industrial applications. The software can