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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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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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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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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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in an optimal way, an issue that will be prominent in industrial, commercial and residential areas across the country. The models and solutions will be developed in a general way in order to be
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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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(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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process. Address blind inverse problems by defining a network to learn distortion functions from data, informing the optimization in the learning process. Refine optimization and learning strategies
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effects of NSPs on poultry performance. Locally sourced ingredients are becoming more prevalent, challenging some of the traditional enzyme strategies in regard to substrate presence and ultimately, optimal