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intensive training in energy modelling, AI-accelerated optimisation, and lifecycle-aware computing. Whether working on smart mobility, sensor nodes, or autonomous platforms, you’ll be contributing to a new
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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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and adaptation Advanced sensors for water quality, wastewater-based epidemiology and point-of-care diagnosis Environmental biotechnology Environmental pollution, water-soil-waste system modelling and
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) modelling, the project will optimise sensor placement and sensitivity. It will also evaluate how strain, temperature, and environmental factors influence hydrogen behaviour. Ultimately, this research seeks
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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