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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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analytical frameworks grounded in Mean Field Game (MFG) theory and Multi-Agent Reinforcement Learning (MARL), which are tailored for eCPS. These frameworks will facilitate the creation of effective control
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engineering or a relevant area. An MSc degree and/or experience and good knowledge in gas turbine theory, thermodynamics, Machine Learning, and computer programming will be an advantage. Funding Sponsored by
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estimation of useful life. In this research, a theory will be postulated for the combined mode of gear failures. The theory will be supported by the basic gear failure mathematics and preliminary validation