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sensing, and Electromyography (EMG) tools to understand user-device interaction and optimize real-world rehabilitation performance. The student will gain experience in AI, human biomechanics, smart textiles
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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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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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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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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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optimise a ‘Digital Twin’ of the Tees estuary to ensure that the NBS are deployed at locations optimal for performance and longevity while operating within the constraints placed upon deployment by other
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multiple objectives in real-time. The complexity of coordinating these distributed systems while ensuring stability and optimal performance presents a significant technical barrier that must be overcome