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on delivering improved accuracy and quantification of resilience in diverse challenging environments, from urban canyons to remote, infrastructure-limited regions. Cranfield University is a specialist
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these sectors, design customized technological solutions using advanced sensing and analytics, and validate these innovations through real-world implementation. The project will bridge the gap between theoretical
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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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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