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processing conditions (e.g. aging and thermo-mechanical processing route) to optimise corrosion performance of Constellium’s high strength alloys. This project is an exciting opportunity to work in close
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physics and diagnostics at the York Plasma Institute Computational combustion modelling using High-Performance Computing (HPC) Machine learning techniques for predictive combustion models Research
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well as cutting-edge research insight. Description The global drive towards electrification in high-performance sectors such as motorsport and aerospace is pushing electric motors to operate at ever increasing
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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environment. Accurately predicting flow and heat transfer in these systems is critical for safety, performance, and design assessments, yet direct high-fidelity simulations, such as Large Eddy Simulation (LES
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, balancing efficiency and sustainability in AI deployment poses a significant challenge, calling for advances in model design and training to reduce environmental impact while maintaining high performance
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be used effectively as a performance digital twin to generate high-quality engine performance models and produce required training data for the proposed project. This could be a good starting point for
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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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Research Group at the Faculty of Engineering which conducts cutting edge research into experimental and computational heat and mass transfer, multiphase flows, thermal management, refrigeration, energy
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Worldwide Dr Marco Colombo Application Deadline: 30 September 2025 Details Are you ready to shape the next generation of high-performance engineering systems? Join a cutting-edge, fully-funded PhD project