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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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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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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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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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the development of hierarchical computational materials discovery schemes combining random structure searching, machine learning, atomistic, and density functional theory (DFT) calculations to accurately and
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’ or ‘internationally excellent’. The highly research active SP Section comprises 13 permanent academic staff with research interests in Bayesian computational statistics and machine learning, uncertainty quantification
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10 minutes and machine learning algorithms to deliver quantitative diagnosis without destroying the samples. The AF-Raman prototype will be integrated and tested in the operating theatre
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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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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