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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
predictive and explainable digital twins. The core challenge this PhD will tackle is how to help digital twins make sense of complex, messy maintenance data and turn it into clear, useful insights
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inclusion and encourages applications from all sections of society. The Faculty of Engineering (FoE) provides a thriving working environment for all Postgraduate Researchers (PGRs) creating a strong sense of
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kW’s) and self-sustainable systems. Ultimately, reducing the costs associated with exploration of remote, extreme maritime environments by realising self-powered robotic systems which recover the ambient
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disruptive aircraft configurations involves combining advanced engineering practices, including computing power, sensing, AI/ML, and system-level engineering. Comprehensive verification and validation
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. This involves 'handcrafted' expert systems, which are good at reasoning about narrowly defined problems, but poor at handling uncertainty and have no ability to learn or abstract/generalise. In that sense