341 computational-physics-"https:"-"https:"-"https:"-"https:"-"BioData"-"BioData" positions at University of Nottingham
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have a positive attitude. Working with academics, researchers and PhD students in managing and conducting research activities across the programme, you should have experience of working collaboratively
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Computer Science at the University of Nottingham is seeking a talented researcher with skills and experience in soft robotics, specifically in interactive materials to augment robot and human bodies
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INTERNAL VACANCY This vacancy is open to employees of the University of Nottingham only. We are looking for a friendly, engaging, enthusiastic person to join the Mathematical Sciences and Computer
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Physical Sciences or a related discipline, with expertise in fluid mechanics and heat transfer. Experience with Computational Fluid Dynamics software, preferably OpenFOAM. Programming skills with software
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achieved Athena Swan Gold Award . To help you succeed, we published Candidate Guidance to provide support on the application and interview process. Discover our benefits, visit Your Benefits website. We
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strategy through evidence-based engagement and collaboration Work closely with senior leaders and external partners on a high-profile programme Contribute to a vision that places students, staff, inclusivity
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. The main purpose of this role is to provide administrative support for research projects at CEBD. This will include supporting the RAPID Eczema Trials programme; a National Institute for Health and Care
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Residential Experience Manager. The successful candidate will be required lead on the delivery of the ResX Living and Learning programme as part of the wider ResX team. This is a wide-ranging role and the
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computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and ML. The University
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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