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reactors and the study of sonochemical reaction engineering. We excel at controlled experiments aimed at determining the driving forces for optimally efficient sonochemistry across different reactor scales
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manufacturing. AI methods will then be applied to this large database to inform optimal processing parameters that will produce components with reliable material properties. The process will be validated in a
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development towards optimizing and understanding sonochemical nitrogen fixation to help advance our internationally leading programme of research. This work will also contribute towards building a case for a
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optimality with computational efficiency. Reinforcement Learning through Stochastic Control. We will develop methods from stochastic control, which will provide a mathematically grounded approach that has a
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advanced numerical simulation and/or optimization tools to the design and characterization of the facility; interact with other technical groups working on the Diamond-II design (e.g. Engineering