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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
amounts of maintenance and operational data, from sensor streams to technical logs, yet much of it remains unstructured, fragmented, and underused. Hidden within these records are insights that could help
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subsurface and internal temperature distributions. Semi-destructive approaches, such as embedding thermocouples by drilling holes, can provide internal data but often disrupt the process, alter the thermal
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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academia and industry. Uncover and quantify critical degradation mechanisms to inform the design of next-generation fusion materials. Translate sophisticated scientific data into impactful insights through
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to inform the design of next-generation fusion materials. Translate sophisticated scientific data into impactful insights through clear communication to diverse audiences, including industry stakeholders and
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those studying a research degree with a wealth of social and networking opportunities. How to apply For further information please contact: Name: Dr Stefano Mori Email: stefano.mori@cranfield.ac.uk If you
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opportunities. How to apply For further information please contact: Name: Dr Andrea Momblanch If you are eligible to apply for this research studentship, please complete the online application form . Please note
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reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing for the collection of data to develop and validate prognostic models for filter
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significantly reduce the amount of vibration data to be stored on edge devices or sent to the clouds. Hence, this project's results will have a high impact on reducing the hardware installation and operation
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that facilitate seamless integration between AI hardware components and embedded systems, ensuring efficient data flow and processing. Cranfield University offers a distinctive research environment renowned for its