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resources, and design facilities, supporting AI-powered electronics research. This project will be conducted within Cranfield’s Integrated Vehicle Health Management (IVHM) Centre, established in 2008 in
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sleep patterns, gaining insights into sleep-related issues, and personalised sleep health management. However, the dynamic nature of sensor networks caused by frequently adding and removing nodes has
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
and reasoning techniques to support predictive maintenance and asset health monitoring •Design feedback mechanisms that deliver interpretable insights (e.g. alerts, recommendations, confidence scores
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highly desirable that you have a sound understanding of robotics, machine learning, and AI. Benefits Attractive salary up to £35,116 per annum Excellent conditions including - social security tax, health
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at the interface between stochastic modelling, signal processing and data science. Ultimately, the project will develop key indices that can be used to assess the health of the soil ecosystem. Such indices
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, organisational and policy context of the National Health Service. The PhD research will focus on how bottom-up networks are involved in promoting change. In recent years, numerous networks of clinicians
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to state-of-the-art laboratories, hardware/software resources, and design facilities, supporting AI-powered electronics research. This project will be conducted within Cranfield’s Integrated Vehicle Health
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Project details: Noise pollution is a growing environmental issue and has become the second-largest environmental cause of health problems in Europe, just after air pollution. Among
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convert relevant measurement data into actionable information, such as the health condition, and/or the remaining useful life of critical assets. Currently, Artificial Intelligence (AI) based big data
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predictive maintenance. Gas turbine diagnostics and prognostics has been progressed quickly in recent years and are crucial technologies to predict the health of gas turbine systems and support the predictive