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exercise science, human nutrition, human physiology, biochemistry, food science or a related subject. A relevant Master’s degree and/or experience in one or more of the above subjects will be an advantage
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. Successful candidates should have a background in applied mathematics/theoretical physics/computer science or related discipline, alongside strong computational skills. Value: Home fees paid and stipend
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, or intervention using electronic, mechanical, or smart material systems. This particular PhD studentship, based at the School of Engineering, University of Birmingham, focuses on developing a microfabricated
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The role The post is part of the Wellcome Discovery Award research programme “Collective Action for Race Equity in Health and Social Care”, which aims to address and dismantle structural factors
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. Ideal Candidate: You’re a highly motivated student with a first-class or upper second-class honours degree (or equivalent) in Engineering, Physics, or Applied Mathematics, or a related field. Strong
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alongside numerical simulations relying on high-performance computing and reduced order modelling. We aim to gain new insights about the physical coherent structures which are most relevant to viscoelastic
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM
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computer science or mechanical engineering. The candidate will have programming experience, particularly on the development of machine learning pipelines. The University actively supports equality, diversity and
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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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engineering or a relevant area. An MSc degree and/or experience and good knowledge in gas turbine theory, thermodynamics, Machine Learning, and computer programming will be an advantage. Funding Sponsored by