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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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automotive and aerospace electrification. Applications for this PhD position are invited at the Power Electronics and Machines Centre, University of Nottingham. Based in a recently built £18M facility
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integrating power electronic converters and electrical machines we can use common structures and systems to greatly reduce, material usage and energy consumption. Through a multidisciplinary research approach
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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, such as computer science, neuroscience, engineering, physics, applied mathematics (or be near to completion of their PhD). Skills in computer programming (especially Python or C++ or Matlab) and
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purposes. We are looking for a confident, organised researcher who can evidence: · A PhD, or equivalent in statistics, machine learning, data sciences, or closely related discipline. OR near to completion
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of solid-state materials, with experience in density functional theory (DFT) and/or machine learning interatomic potentials. We welcome applicants with a broad range of research interests and experiences who
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a mixture of computational, analytical and machine learning approaches to model the heat transfer to fuels and their physical and chemical behaviour, including changes in chemistry and physical
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This exciting opportunity is based within the Power Electronics and Machines Control Research Institute of the Faculty of Engineering at the University of Nottingham which conducts cutting edge
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AI hardware beyond traditional computing architectures. Gain a unique combination of skills in mathematics, machine learning, and photonics. Be part of a multidisciplinary research team spanning