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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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, Skills and Experience • You will have substantial technical experience in time series analysis, ideally either in neurophysiology data or wearable sensor data • You will have experience of at least
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the performance of novel, renewable, wave energy harvesting approaches. Here the research ambition is to extend the state of art from small scale sensor networks (nW’s to mW’s), towards a vehicular scale (W’s to
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gas turbine sensor data, if available, will be utilized to validate the developed digital twin in order to estimate non-measurable health parameters of major gas path components, including compressors