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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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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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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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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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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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-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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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
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industrial partner will enhance the transferable skills of the student, such as technical communication. It is expected that industrial data will be used for model validation, building skills relevant to data