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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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master degree in a related discipline (such as aerospace, Robotics). Applicant is expected to possess knowledge and experience in all or most of the following topics: Control theory, Probability theory
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estimation for battery management systems for lightweight lithium-sulfur batteries and have specialist expertise in modelling, control and estimation theory, system identification and computer
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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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. The enhanced image quality will support earlier and more reliable detection of eye diseases. Combining artificial intelligence with mathematical modelling, this non-invasive, cost-effective approach has
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Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we are members of the Women’s Engineering Society (WES) and Working Families, and sponsors of International Women in Engineering Day. We
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are committed to progressing the diversity and inclusion agenda, for example; gender diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we
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of the start of the PhD Award). A demonstrated background in communication theory, networking, and AI would be a distinct advantage. Funding This studentship is open to UK and international students
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; gender diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we are members of the Women’s Engineering Society (WES) and Working
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, Rest of world Reference numberSATM552 Entry requirements We seek highly motivated candidates with: 1. A strong background in robotics, optimal control theory with understanding of uncertainty modelling