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of good-quality data is typically limited for high-value critical assets. This PhD project will focus on developing, evaluating, and demonstrating physics-informed machine learning or domain knowledge
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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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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
artificial intelligence, machine learning, data analysis, or digital systems would be advantageous but is not essential. We value curiosity, problem-solving ability, and a proactive attitude toward learning
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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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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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engineering or a relevant area. An MSc degree and/or experience and good knowledge in gas turbine theory, thermodynamics, Machine Learning, and computer programming will be an advantage. Funding Sponsored by
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems