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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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health monitoring, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical expertise, enhancing their research capabilities
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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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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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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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We are pleased to announce PhD studentship project in “Advanced Composites Development for Hyper-velocity Impact Protection of Space Satellites Structures”. This is an exciting PhD research
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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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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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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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Join our diverse and inclusive team to transform the future of aviation as part of the UK’s EPSRC Centre for Doctoral Training in Net Zero Aviation. Offering fully funded, multidisciplinary PhD