339 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" PhD scholarships in United Kingdom
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reservoirs. By embedding governing equations and boundary conditions directly into machine-learning models, the project aims to enable efficient exploration of high-dimensional parameter spaces without
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-driven AI models that capture the underlying process–structure–property relationships governing metal additive manufacturing. By combining mechanistic modelling, in-situ sensing, and machine learning
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potentially druggable targets. Depending on interest, the student will have an opportunity to contribute to other projects within the team and learn a range of important techniques such as cellular, animal
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by the National Institute for Health Research UCL UCLH Biomedical Research Centre are available within the Institute of Health Informatics. The studentships will commence from Oct 2026. About the
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the supervisor’s complimentary research expertise in this area (https://millerresearchgroup.co.uk & https://www.lovelockresearchgroup.co.uk),[8,9 ] this PhD will involve the design and chemoenzymatic synthesis of a
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from working closely with its team of post-docs, associated researchers and partners (that range from Microsoft Research to the NHS). For this project you should have a strong interest in AI/Machine
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bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 30.04.2029 Reference no.: 5311 Your responsibilities: As a University assistant, you will contribute to the work group Machine Learning
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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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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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, release kinetics under biologically relevant triggers. The successful candidate will work at the interface of organic synthesis, chemical biology, and machine learning to guide linker design and optimise