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professional network spanning academia, industry, and national research centres. Through this multidisciplinary project, the student will develop expertise in: Contribute to the development and operation of
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turbine health monitoring. The new knowledge will be very useful to guide condition-based operation and predictive maintenance of future gas turbine engines. The student will be based within the Centre
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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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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
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to the Net Zero targets. In consultation with the wider CDT community, the work will also include the development of a roadmap for the maturation of the technology and the processes required to have it adopted
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AI-electronic systems, ensuring secure communication and operation. Side-Channel Attack Mitigation: Implement techniques to protect systems against side-channel attacks, safeguarding sensitive
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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project would suit students with a background in electronics, embedded programming, signal processing, vibration measurement and analysis, maintenance engineering, and electro-mechanical engineering
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evaluation. Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle
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. Understanding the process of droplet impact and freezing dynamics at high airspeeds, on textured and non-textured surfaces is critical to deciphering the physics behind ice adhesion and accretion. Previous work