28 postdoc-machine-learning PhD positions at University of Nottingham in United Kingdom
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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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Failure Analysis of Composite Sleeves for Surface Permanent Magnet Electrical Machines This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites
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PhD Studentship: Electrical Machine Architectures for Next-Generation NetZero E-Mobility. the University of Nottingham This project offers an exciting opportunity to undertake cutting edge research
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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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including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during their PhD
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Open PhD position: Autonomous Bioactivity Searching Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 42-month funded PhD studentship will contribute to cutting
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robotics, and materials science. Project description: 3D-printing of soft robotics is a growing field, with many applications in biomedical devices, electronics, and autonomous machines. Actuators to drive
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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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offers an exciting opportunity to undertake cutting edge research in electrical machines within the globally renowned Power Electronics, Machines and Control (PEMC) Research Institute , University