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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
based within the Manufacturing, Materials and Design theme at the Centre for Digital and Design Engineering (CDDE), which offers access to advanced simulation, visualisation, and high-performance
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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for High-Performance Computing and Future Data Centres 1- AI-Optimized Electronics for Edge and Cloud AI Acceleration – Investigate AI-enhanced data centre electronics, optimizing workload distribution
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for the collection of data to develop and validate prognostic models for filter degradation. Integrated Drive Generator (IDG) Rig: Simulates the operation of an aircraft's IDG, used to investigate fault detection
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be used effectively as a performance digital twin to generate high-quality engine performance models and produce required training data for the proposed project. This could be a good starting point for
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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
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testing, enhancing both computational and experimental skills. Additionally, the possibility of contributing to cutting-edge research in a high-impact field means that the student will be part of a
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institutions. State-of-the-art facilities: Access advanced laboratories and high-performance resources. Flexible learning: Tailored research projects aligned to personal interests and career aspirations. Career
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to capture material properties and performance in the context of the disruptor utilisation, To employ integrated computational and experimental optimisation of material and geometrical attributes with respect
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Aviation by 2050. This exciting doctoral project, in collaboration with Rolls-Royce, will develop innovative computer vision methods which when combined with optical flow velocimetry will enable imaging