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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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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
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paid. We expect the stipend to increase each year. Only Home students are eligible for funding. The start date is October 2026. The project aims to develop and optimize metal oxide aerogel materials
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the development of system software. Key questions include how LLMs can support programmers in writing complex logical code, generating high-quality tests, and optimizing performance. Moreover, when integrated with
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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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sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems
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environmental inputs, algae physiological parameters and microbial community eDNA data to develop predictive mechanistic models which can be utilised to develop an optimal cultivation strategy. The project is
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safety questions: Determining optimal stored energy requirements for grid support, considering various timescales and power ratings. Reviewing and benchmarking storage technologies (lithium-ion batteries
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still hampered by: Ability to detect areas along the intertidal for optimal restoration3. Knowledge on how positive species interactions can be harnessed for rapid restoration4. Availability of devices