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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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A position exists for a postdoctoral Research Assistant/Associate to work on the development of novel semiconductor optoelectronic and sensing devices using high-throughput fabrication and
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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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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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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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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
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. This PhD project investigates how emerging Next-Generation Networks (NGNs), including Beyond-5G (B5G) and 6G, can be connected to ensure secure, high-performance mission-critical communications, vital
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PhD Studentship: Development of Next-Generation High-Performance Titanium Alloy for Aerospace Applications Funder: EPSRC and Rolls-Royce plc Duration: 3.5 years Supervisors: Professor Nick Jones and