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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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their operational lifespan. A key aspect of the project will be the incorporation of communication security measures, specifically targeting resilience against jamming and spoofing attacks. Students will investigate
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investigate strategies to enhance communication security, focusing on resilience against jamming and spoofing attacks. Students will work on designing secure architectures that ensure data integrity and system
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to High-Fidelity Simulations – The project will use OpenFAST, FAST.Farm, and Digital Twin simulations for AI model validation. The student will have the opportunity to join a vibrant community and team
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horizons. It plays a pivotal role in the £65 million Digital Aviation Research and Technology Centre (DARTeC), leading advancements in aircraft electrification, autonomous systems, and secure intelligent
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-solving, teamwork, and communication—setting the stage for a thriving career in research and innovation. This project is situated within the evolving field of cyber-physical systems (eCPS), which has
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performance diagnostic and prognostic technologies and a digital-twin system to support condition-based predictive maintenance of gas turbine engines. The project will be partially funded by Cranfield
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accelerated in the last few years along with the booming of digital technologies. One of the key elements for a successful implementation of the PdM strategy is the usage of technologies that can effectively
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learning, this research contributes to the growing field of digital healthcare, which aims to enhance clinical decision-making and improve patient outcomes. The primary focus of the project is to develop and