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The world is moving rapidly toward renewable energy. Hydrogen is at the forefront as a clean fuel, but its safe storage and use at high pressures require advanced, reliable technology. By joining this project
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project will develop novel methods for modelling and controlling large gossamer satellites (LGSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. The candidate will
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deep learning methods to enhance the predictions beyond existing data. By incorporating microstructural features into predictive models, the aim is to create a reliable data-driven modelling framework
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useful life of electronic components, supporting studies in electronic system reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing
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developing novel composite materials with superior ballistic and hyper-velocity impact protection. The PhD research will help improve the reliability and longevity of space satellites. Composite materials have
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infrastructure components, causing potential reliability, safety, and longevity issues. Addressing this critical issue represents a vital area of research in aerospace materials science and hydrogen engineering
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Supervisory Team: Dr. Jie Yuan, Dr. David Toal PhD Supervisor: Jie Yuan Project description: Robust design is crucial to ensure the durability and reliability of aerospace components throughout
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. While autonomy is becoming more integrated into modern mobility, the reliability of Position, Navigation and Timing (PNT) systems—especially in environments where GNSS signals are denied or degraded
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health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven
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require advanced, reliable technology. By joining this project, you’ll be developing innovations critical to a greener, more sustainable future.About the Leonardo Centre The Leonardo Centre at the