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Funding notes: Industrial CASE, EPSRC supported by EDF Energy. The candidate need to either be UK national or UK resident. Will provide a stipend of at least the standard UKRI rate. (24/25 £19,237
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alternative models where existing methods prove inadequate. This project is suitable for Engineering or Physics graduates with a strong background in fluid mechanics and heat transfer, preferably with
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undertaking the following modes of study: Subject restrictions This funding is available to students undertaking study in: Accounting and Finance Business and Management Science, Technology and Innovation
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of the GDPR, Federated Learning (FL) has emerged as a leading privacy-preserving technology in Machine Learning. Despite its advancements, FL systems are not immune to privacy breaches due to the inherent
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confidence level and required computational efficiency. Recent advances in Machine Learning (ML) offer promising means for developing hybrid ML-based ECTM models that can overcome computational deficiencies
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involve close links to experimentalists with the chance to test out results at leading XFEL facilities in Europe/USA. Outcomes will include an enhanced understanding of stochastic processes like ion hops in
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conditions, these products end up stuck to the sides of the drier (see picture above), leading to wastage and the need for expensive manual removal and cleaning of the equipment. The drying process is complex
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) in a relevant subject (Physics, Chemistry, Materials Science, Chemical Engineering), experimental track record and willingness to learn. Home rate fees are fully funded. Applicants from overseas will
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Engineering and Analytical Science Civil Engineering Computer Science Electrical and Electronic Engineering Management of Projects in Engineering Mechanical Engineering Academic requirements To be considered
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reward mechanisms that can incentivise participants to contribute to the training process. Entities should be fairly compensated based on their contributions, which requires developing methods to assess