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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will
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on performance and safety, for example, through the efficient computation of Lyapunov and barrier functions, forward and backward reachable sets, optimal value functions etc. The broad goal is to build upon recent
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
investigate the large area production of graphene, BN, MoS2 and other layered materials, optimize their transfer process in view of their application in energy, electronics and photonics. This will include
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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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Solutions, you will apply your methods in detailed simulation studies using Imperial’s high-performance computing facilities, assessing performance in realistic scenarios. You will regularly present your
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Funding: School of Computer Science studentship consisting of the award of fees, together with a tax-free maintenance grant of £20,780 per year for 3.5 years. Lead Supervisor’s full name & email
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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Engineering, and Engineering Management. Students with interests in computational mechanics, optimization design, bioinspired design, sustainability management, machine learning, AI, uncertainty quantification
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optimization. An ideal candidate is expected to have a strong interest in theoretical and innovative research. To apply. Please contact the supervisor, Dr Chao Chen - chao.chen@manchester.ac.uk . Please include