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, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical expertise, enhancing their research capabilities and employability in
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for research into thermal management and system health monitoring, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. •Specialist training in AI, machine learning, and digital engineering. •Collaboration with academic and industry experts for technical insight and mentoring. •A supportive research environment focused on both
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. Simulations are suitable to characterise processes in healthy and diseased individuals including stroke patients. Machine learning methods might be considered to accelerate simulations. The project provides a
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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Technology. Mr Kumar is the module leader for Military Vehicle Dynamics, part of the Military Vehicle Technology MSc, which he teaches in the UK and overseas. He worked on project from the UK Ministry of
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experimentation and validation, and machine learning. References of our current/recent work are here: "Automatic Retrieval-Augmented Generation of 6G Network Specifications for Use Cases," IEEE Communications
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Machine Learning-based diagnostics and prognostics digital twin system will be developed, aiming to provide fast and reliable predictions of the health of gas turbine engines. Non-confidential operational
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offers a modular, cohort-based training programme with emphasis on innovation and impact, collaborative working and learning, continuous development, active engagement with partners and stakeholders and
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significant impacts on reducing machine downtime in many industrial applications, including steel casting, paper mill, food industry, wind energy, etc., which leads to reductions in operation and maintenance