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numberSATM533 Entry requirements Applicants must have a B.Sc. in electronic / information engineering or computer science and must either have or close to having a Master’s degree (must be completed by the time
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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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gas turbine sensor data, if available, will be utilized to validate the developed digital twin in order to estimate non-measurable health parameters of major gas path components, including compressors
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zone in a very complex manner and lead the modelling to an imperfect zone of assumptions. These complexities allow the researchers to use approximations for useful lifetime calculations. Based
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treatment. It is essential for candidates to hold a driving licence (incl. an international licence) as the role involves some field sampling around the UK. The candidate should be self-motivated, have good
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training programme in respect of industry-specific skills, and access to hotfire facilities at Westcott, Machrihanish, and elsewhere. You can learn more about the programme at r2t2.org.uk. Kick stages are a
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opportunities to access some of the MSc courses and CPD short courses relevant to the research and attend and publish papers in international conferences. Cranfield operates a substantial Doctoral Researchers
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. Despite some success stories of the use of ultrasound/AE-based technologies for CM of low-speed bearings, high investment cost for hardware and software is the main bottleneck in adopting these technologies
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
at scale? Digital twins offer a promising foundation, but to truly support engineering decisions, they need to go beyond simulation and begin to interpret and reason about the systems they represent
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, reliability, and environmental resilience. The proliferation of intelligent systems has led to increased energy consumption, raising concerns about sustainability and operational costs. Energy-efficient