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, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Strong background/skills on machine learning, mathematics, probabilistic
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solutions that extend the operational life of devices and reduce environmental impact, applicable to areas like smart grids, electric vehicles, and portable electronics. Research Focus Areas: Power-Aware
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(i.e. red agents). However, due to a fragmented market, rapid technical developments, and nascent research the extent of capabilities and optimal solution architectures are not well understood. Current
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the growing demand for Electric Vehicle (EV) batteries. Highly reconfigurable robotic cells powered by AI promise to deliver the new generation of resilient manufacturing systems. However, real
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A continual learning approach for robust robotic control in electric batteries assembly. This project is an exciting opportunity to undertake industrially linked research in partnership with
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often fail to preserve the fidelity of combined datasets, leading to loss of crucial information. This proposal aligns directly with the CAMS Data Analytics Theme and the Grand Challenge of using machine
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and decentralized manufacturing, current systems are constrained by the requirement that the printer’s workspace must be larger than the product itself. As a result, large end-products are typically
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
expertise, access to real-world data, and a clear pathway for the industrial application of research outcomes, ensuring strong alignment with current and future industry needs. A major challenge facing
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failures before they occur, enabling proactive maintenance strategies. Anomaly Detection Mechanisms: Implement machine learning techniques to identify and classify anomalies in electronic systems, enhancing
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the need for sustainability to achieve Net-Zero goals. Cyber-Physical Systems (CPS) integrate machines, robots, and AGVs, but challenges like mechanical wear and electronic errors pose risks to efficiency