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development of optimal passive mitigation solutions through the enhancement of components such as elastomers. These elements will be selected and optimised to absorb and isolate shock and vibration while
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methods to improve systems, and ML research develops such methods. Major gains are made when the development of ML and systems are symbiotic and co-optimized. This is relevant across a broad spectrum of
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the optimization-based methods (doi.org/10.1016/j.apenergy.2020.116152 ), 3- Weakness of the model-predictive-control (MPC) against HESS’s parameters uncertainties, noises, and disturbances (doi.org/10.2514/6.2022
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. The research group is seeking a talented Doctoral Researcher in nonlinear systems and control with strong interest in nonlinear stability theory, modeling & identification, optimal control, certifiably safe
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also throughout the development phase, which involves transforming a molecule into a medicine and addressing various chemistry, manufacturing, and control (CMC) challenges. A key aspect of this process
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Associate Support development and documentation of Python-based Design Tool workflow for D-Suite Project Formulate and solve optimization problems, developing algorithms which have appropriate accuracy
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Application deadline: All year round Research theme: Systems and Control How to apply: uom.link/pgr-apply-2425 This 3.5 year PhD project is funded by The School of Engineering and is available
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. The research group is seeking a talented Doctoral Researcher in nonlinear systems and control with strong interest in nonlinear stability theory, modeling & identification, optimal control, certifiably safe
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You will join the EPSRC-funded project “Behavioural Data-Driven Coalitional Control for Buildings”, pioneering distributed, data-driven control methods enabling groups of buildings to form
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system