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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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drive the gradual development of these technologies toward real-world applications. This involves engineering experimental hardware for cell culturing workflows, optimizing experimental processes, and
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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
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, complexity, and harsh operating conditions. This PhD research addresses two critical challenges in this domain: (1) optimizing sensor movement for inspecting large and complex equipment using robots and
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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
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store energy by exploiting quantum phenomena (for example, by exploiting entanglement) in order to improve the performance of the device. There are still many questions surrounding the optimal
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(Dr Jun Jiang) (2) In-situ formability, microstructure analysis and forming process optimization (Prof Li-Liang Wang) (3) Crystal plasticity modelling to understand how microstructural features caused
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reactors, can be optimized for N2O mitigation or ultimately complete N2O removal. Overall, the project represents a unique opportunity to engage with the water utility sector with regards to greenhouse gas