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operation of integrated converter-motor systems. Research environment Applications are invited to join the Power Electronics, Machines and Control (PEMC) Research Group at the University of Nottingham. Based
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operation of integrated converter-motor systems. Research environment Applications are invited to join the Power Electronics, Machines and Control (PEMC) Research Group at the University of Nottingham. Based
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on solving challenges at the interface of Synthetic Biology, Control Engineering, AI, and Robotics, and their application to global challenges across biotechnology. The approaches taken will depend
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be carefully controlled and optimised to ensure good electrochemical performance, mechanical integrity, and impermeability. This challenge is compounded by the lack of rapid inspection methods capable
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with improved diagnostics, prognostics, and control for automotive applications. Application process Please contact us directly at [email protected] before applying. Apply directly via
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control strategies. Current steel processing typically has very tightly controlled processes with little variability and control approaches optimised to minor changes. The combined challenges of cost
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to simultaneously learn control policies and safety certificates—mathematical proofs that control decisions are safe. Data from system operation provides evidence that both the control and the proofs
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PhD Studentship: Designing Human-AI Teams for Meaningful Human Control of 'Machine-Speed' Operations
be applicable to command teams working in uncertain and information-degraded environments. Meaningful human control is necessary when humans have to be retained in or on the loop. This requires the AI
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an incurable disease. This project will investigate how B-lineage transcription factors (TFs) and complexes involved in chromatin activation and organisation (such as Mediator, P300/CBP, CTCF etc) control gene
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sites, highly engineered geological repositories or in the marine environment. However, redox controls on porewater chemistry in these environments is poorly understood and requires robust datasets