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Field
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modelling to provide a robust framework for integrating nature-based solutions into SO management. This can alleviate the pressure on treatment infrastructure and reduce dependence on grey infrastructure
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how variations in mould structure, porosity, and surface characteristics affect radiative heat transfer and casting performance. Phase-field modelling will also be used to simulate defect formation and
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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comprehensive model of what tranquillity is, the factors that influence it and how to design for it. Attention to design contexts and design processes will be key to ensuring that useful measurements, methods and
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recovery in critical applications, including aerospace, healthcare, and industrial automation. Research Focus Areas: Predictive Analytics for Fault Detection: Develop AI models that predict potential system
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mixed research methods—including behavioural surveys, environmental monitoring, and dynamic thermal modelling—the project aims to generate retrofit strategies that improve energy efficiency, reduce carbon
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community-led model. Investigate how knowledge is co-created and used across different scales (individual, organisational, systems). Compare the Isles of Scilly CRN with eight other CRNs across the UK, each
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season properties (e.g. number, intensity) for lead times ranging from one to approximately six months in the latest generation of dynamical seasonal and decadal forecast models. Seasonal forecasts
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vulnerabilities. Frontier models show superior performance when combined with a focused knowledge base and multi-agent architectures. However, in most cases human involvement is still required, and fully autonomous
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process, and this process itself can impede certain policy. This project involves summarising models of political choice (e.g. the median voter, probabilistic voting, citizen candidate, etc models) with a