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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 2 months ago
. The research will be computational based, and at this stage is still broad, so we can formulate the optimal plan for the right candidate. We will take an interdisciplinary approach, and you will be able
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personalized. The candidate will investigate textiles, computational design, and functional validation of hand-worn assistive devices, aiming to create textile exoskeletons that can conform to complex hand
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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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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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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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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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the power of AI/ML and software-defined networking (SDN), and distributed learning methodologies, the research will focus on creating self-configuring, self-optimizing, and self-healing mechanisms for real
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mapping using a team of highly mobile legged or legged-wheeled robotic platforms. The research will investigate advanced algorithms for multi-robot coordination, dynamic path optimization, and collaborative