20 security-"https:"-"https:"-"https:"-"https:"-"Robert-Gordon-University" PhD positions at University of Nottingham
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to become an intrinsic part of the global integrated energy system. However, this kind of technology has not yet achieved widespread commercial adoption due to electrochemical systems’ reliability, safety
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PhD project: Modelling Reliability and Resilience of Hydrogen Systems for Improved Safety and Sustainability Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering
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and toxicity performance make them strong candidates for safety‑critical applications in aerospace, rail, automotive and battery technologies. However, current PFA systems suffer from brittleness
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promising sustainable alternatives to conventional epoxy systems. Their excellent thermal stability and favourable fire, smoke and toxicity performance make them strong candidates for safety‑critical
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sponsors own security checks before starting the PhD. Start date: 10 April 2026 Closing date: 15 May 2026 For further information please email Professor Chris Gerada (University of Nottingham) and Will
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, the position is only available for UK home candidates. As sponsored by MTC, the successful candidate would need to pass the sponsors own security checks before starting the PhD. Start date: 5 October 2026
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available for UK home candidates. As sponsored by MTC, the successful candidate would need to pass the sponsors own security checks before starting the PhD. Start date: 10th April 2026 Closing date: 15th
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the power Sector to ensure the reliability and safety of high-temperature industrial materials and components. How to apply Application deadline: 31-May-2026 To apply, please email your CV and supporting
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, safety, and cost. One of the most common causes of development delays is the presence of technical silos between specialised teams. Because the disciplines are tightly interconnected, a small change can
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, robust, and trustworthy when deployed in real-world, safety-critical environments. Together, we will advance the foundations of intelligent autonomous systems by combining modern reinforcement learning