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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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, via the admissions portal (accessed via the 'Apply' button above) by midnight (23:59 GMT), 19th August 2025. On the ‘Choosing a programme’ page, please select (Neuroimaging) Research MPhil/PhD (Full
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experience and ambitions A Master’s degree (or equivalent) in physics: photonics, AMO physics, quantum optics, or a related discipline; Excellent communication skills (fluency in English) and strong
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well as collaborate with others with effective written/oral communication skills. Knowledge of fluid mechanics or CFD is essential. Experience in programming (Fortran/C++/Python) and knowledge of acoustics and solid
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or carried out their main activity in the UK formore than 12 months in the 36 months before the recruitment date. Must hold a Master’s degree by the start date (September 2025). Good English proficiency
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degree or higher) • UK home student status holder • Excellent oral and written communication skills in English • Strong problem-solving abilities • Interdisciplinary data science and energy interests
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Excellent oral and written communication skills in English Strong problem-solving abilities Interdisciplinary computer science interests Ability to work independently and in a collaborative environment
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with: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in
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-funded doctoral training programme dedicated to academic research in space propulsion. R2T2 PhD programmes are already underway at nine UK universities, and the programme overall is centred on the Westcott
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins