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physical laws, or an implicit form of extra data examples collected from physical simulations or their ML surrogates. In medical domains, patient data is typically distributed across multiple hospitals
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. An optimisation tool has been developed that uses a genetic algorithm to optimise the location of BGI taking surface water flood risk reduction and the cost of different interventions into consideration. This PhD
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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learning (ML) for high-fidelity data ‘stitching’. The integration of data from multiple analytical platforms is critical for advancing the understanding of complex biological and chemical systems. This work
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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Most of chemical products consist of multiple compounds that are formulated together, of which the development process is iterative, laborious, and complex. The formulated products sector
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electronic converters are required to connect renewable energy sources and energy storage systems to the power network. These converters employ sophisticated control algorithms that must simultaneously achieve
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy