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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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models. This theoretical project will facilitate close collaboration with experimental groups and enable benchmarking of theoretical predictions. The PhD researcher will be part of the Correlated Quantum
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for household who stay indoors, and to prepare for emergency responses. Possible quantitative methodologies include concurrent time-series analysis of outdoor and indoor environment data, prediction model
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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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ultrasonic sensors for real-time seal gap measurement. Combine experimental research and mathematical modelling to pioneer innovative sealing solutions. What We Offer Full 4-year Funding for UK applicants
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trustworthy operation of navigation systems in complex, GNSS-denied scenarios. The ultimate goal is to provide the navigation research community and industry with tools and methods that ensure continuous, high
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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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will also include evaluating and validating existing numerical models, ensuring their reliability in predicting real-world conditions. This project is supported by brand-new laboratory facilities
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several benefits, including thermal conductivity, electrical insulating and creating the necessary structural integrity needed around the battery. However, this process can be slow, induces an element of
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cloud data centres. However, this can come at the cost of performance and slower function processing, which manifests as higher energy consumption and increased operational costs (OpEx) for operators. So