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machine learning algorithms and to assess when AI predictions are likely to be correct and when, for example, first principles quantum chemical calculations might be helpful. Predicting chemical reactivity
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to the Power Electronics, Machines and Drives Research Group (PEMC). Your research will focus on electrical machines, drives, design, materials, thermal management, control, and testing. The purpose of the role
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About the Role A fantastic opportunity has arisen for a Senior Research Fellow to join the Power Electronics, Machines and Drives Research Institute (PEMC) at the University of Nottingham and become
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research in areas including Artificial Intelligence, Big Data and Visual Analytics, Computational Intelligence, Machine Learning, Software Implementation and Testing, and their applications in manufacturing
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the university, genomic and metabolomic measures, offering novel potential to explore the physiological basis for imaging measures and apply machine learning in a radiological context. You will join an established
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and machine learning, uncertainty quantification, Bayesian non-parametrics, image analysis, geometric statistics, and stochastic processes with an internationally leading research group in epidemic
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in this state-of-the-art facility for advanced electrical machines for power generation, marine, aerospace and automotive industries. About You Candidates should hold or be shortly due to obtain a PhD
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facility for advanced electrical machines for power generation, marine, aerospace and automotive industries. About You You should have a PhD degree, or equivalent, (or be near to completion), in electrical
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Applications are invited for a full-time Research Associate/Fellow positions within the George Green Institute for Electromagnetic Research and Power Electronics Machines and Control research groups
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expertise in MRI, biomaterial characterization, computer vision, and machine learning with applications in biology, food science, and oral health. The research leverages cutting-edge resources from