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failures before they occur, enabling proactive maintenance strategies. Anomaly Detection Mechanisms: Implement machine learning techniques to identify and classify anomalies in electronic systems, enhancing
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from such machines to derive algorithms expressing their state of health and next maintenance needs. A background in both engineering and machine learning would be useful, although help is readily
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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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have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge of interdisciplinary
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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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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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flow of realistic car shapes and their implications on pollutant dispersion under actual road conditions. Overview The PhD student will test these two hypotheses: There is a strong effect of the vehicle
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degree or equivalent in a related discipline. This project would suit individuals from a variety of backgrounds such as machine learning, statistics, and economics. Candidates with experience or keen
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in computer vision would be beneficial but not essential; determination, curiosity, and a willingness to learn are key attributes we value. Applicants with alternative qualifications, industry