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additional training costs; A comprehensive individual training programme, across all years of the award; A funded three-month placement opportunity. How to apply You should first approach a potential lead
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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, but current methods are not always efficient or optimal. The process lacks an intelligent, informed approach to selecting the best grinding parameters, which can lead to inefficient maintenance actions
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background and an interest and expertise in physical, computational and/or analytical chemistry. Please note that this is a 4-year Graduate Teaching Assistant (GTA) studentship, with a slightly higher teaching
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collaborative studentship between the University of Edinburgh and the National Quantum Computing Centre (https://www.nqcc.ac.uk ). The position will be registered and hosted at the University of Edinburgh and
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, but it would be possible for those with mathematics, physics or other computational training to learn the healthcare context Experience in one or more of the following would be desirable: Programming in
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to apply: Please choose Electrical and Electronic Engineering Research Program and Control and Power Group, then indicate Professor Balarko Chaudhuri as a potential supervisor when making the application
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accuracy is still limited. In contrast, computational fluid dynamics (CFD) models can capture the arc physics and molten pool dynamics, including arc energy transfer and liquid metal convection within
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have to also apply successfully to the ARTS Doctoral programme during the six-month probation period for the position. The selected applicant is thus expected to to be able to develop a high quality
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: Functional genomics and psychiatric epidemiology Clinical informatics and data harmonisation Co-production and participatory research methods Working within a cutting-edge UK-wide data infrastructure