355 computational-physics-"https:"-"https:"-"https:"-"https:"-"L2CM" positions at University of Nottingham
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in the delivery of technology transfer events The successful candidate will have the following; Experience in the maintenance and operation of feeding equipment and working with dairy cattle Computer
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, Cambridge, Dundee, UCL and Southampton and is a comprehensive translational program for improving research, clinical care and quality of life for those living with rare lung diseases. The main aim
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/physics or any related discipline. This is a largely experimental research project based at the University of Nottingham, with some aspects of material modelling and development of machine learning to aid
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characterisation and AI‑assisted modelling. Working within the Composites Research Group, you will develop a digital twin of the PFA cure process, combining mechanistic modelling with neural‑network‑based prediction
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this information-gathering process. The successful applicant will have strong expertise in programming, and in particular developing AI-based computer vision methods. Ideally, they will have experience
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programme in the field of political science with a focus on quantitative methods. The role holder will conduct original research, resulting in publications in internationally recognised peer reviewed journals
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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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. If appointed, you will be responsible for managing events and overseeing our gallery spaces, leading teams of ushers and invigilators, and ensuring the successful delivery of Lakeside’s diverse programme of
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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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develop a digital twin of the PFA cure process, combining mechanistic modelling with neural‑network‑based prediction of complex behaviours such as void formation and brittleness. In parallel, you will