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collaborative programme of research funded by the Aerospace Technology Institute (ATI) with several Industry partners, including Airbus, GKN and Renishaw. Critical for the implementation of additive manufacturing
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processing, data analysis, data-driven modelling, optimisation and computation algorithms, machine learning models and neural network structures, as well as strong skills and experiences in computational
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hardware-in-the-loop (HiL) techniques and ML algorithms for the accurate and on-time detection of faults, so that failures can be prevented by alerting the end-users and diagnosticians during periodical
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services including data sensing, information flow and processing, and associated information decisions with computational intelligence and control. The increasing size and complexity in cyber systems pose
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manipulation strategies. Develop intelligent algorithms for the robotic execution of contact-rich manipulation tasks, enhancing adaptability, precision, and efficiency in a manufacturing context. Collaborate
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/adaptive algorithms, offline and online data analysis, conducting experimental research, and online evaluation of the developed adaptive strategies with a robotic application. The prospective students can
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intelligent sensing, followed by detection of the important events.In the light of autonomous decision making, the project aims at developing machine learning algorithms for knowledge extraction from data
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complexity, classical control techniques cannot be easily applied because of computational bottlenecks or an absence of suitable prediction models. Distributed control approaches have been conceived to handle
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algorithms that would allow the delay and/or suppression of hysteresis effects in dynamic stall through the use of control surfaces, for example, allowing the safe recovery of aircraft from post-stall
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adapted based on the abilities and needs of patients. Moreover, automatic intelligent algorithms will be developed in to make the control intuitive, natural and adaptive. Such that the model can learn new