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modelling tools (CST or HFSS) - Fabricate and test for optimal electromagnetic performance, such as bandwidth, return loss, insertion loss and power-handling. - Develop and characterize new bonding/alignment
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techniques. This research proposes a novel framework that integrates Machine Learning (ML) for structural health monitoring (SHM) and design optimization of CFDST wind turbine towers. The study will focus
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become the bottleneck in achieving optimal performance and trustworthiness. This project will focus on how a federated multi-task learning framework can be effectively designed and optimised to address
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2:1 undergraduate honours degree in a relevant subject and meet our English language requirements. They should have a strong background in physics and/or mathematics (e.g., PDE, optimization) and/or
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its environment and respond optimally in dynamic operating conditions. Meanwhile, you will also develop intelligent control strategies that minimise energy use while ensuring punctuality and safety
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while discarding those that contribute minimally, thus reducing the model's order without compromising accuracy. The framework will employ advanced techniques such as machine learning, optimization
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seek optimal trade-offs between compactness and performance, delivering foundational insights into the future of high-performance electric propulsion systems. Funding 3-year PhD tuition fee (for UK home
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alloys), and additive manufacturing to push performance boundaries. The research will seek optimal trade-offs between compactness and performance, delivering foundational insights into the future of high
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machine structures, together with AI-driven optimization frameworks for diverse applications while considering LCA metrics. The success of this project could serve as a model for other energy-related
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storage systems (ESSs), and electric vehicles (EVs) that collectively form a local energy community (EC). ECs are supposed to facilitate direct peer-to-peer (P2P) energy trading mechanisms to optimize