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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
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implementation of decentralised personal data governance frameworks? Technology: How can multimodal knowledge graphs in combination with generative AI help individuals govern their data (e.g. within decentralised
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theories. At Swansea University, our group has played a leading role in uncovering new classes of such systems—known as integrable deformations—and in exploring their relevance to string theory, quantum
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interest in expanding their knowledge in both domains. (1) Geometry/Topology -related methods in computer science. (2) Machine Learning. (For example, graph neural networks, generative networks, or neural
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theory to lightweight on-hardware prototypes, with publications targeted at leading IEEE venues in communications and signal processing, and relevant AI venues. Indicative directions (choose one or combine
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theory to application, and enables simulation, measurement, and fabrication of metamaterials and metamaterial-based devices. Entry requirements The successful candidate should be completing / have
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experiment, theory, and numerical simulations in the department of physics at the University of Exeter. The research question is how to effectively shape electromagnetic radiation when the wavelength reaches
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins
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, providing a unique opportunity to contribute to both the academic literature and the NHS’s service provision. The research has its theoretical base in the broad tradition of Consumer Culture Theory (CCT) and
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems