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application no later than August 1, 2025. Project description Linear algebra expressions are evaluated in an efficient and robust way by mapping them to a carefully chosen sequence of calls to optimized
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in electrical machines within the globally renowned Power Electronics, Machines and Control (PEMC) Research Institute , University of Nottingham. The project will be supported by the state-of-the-art
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multidisciplinary research in energy markets, optimization, game theory, and machine learning. Our team of 13 members (link ), from 10 different nationalities, values diversity and includes experts from a range of
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This project offers an exciting opportunity to undertake cutting edge research in electrical machines within the globally renowned Power Electronics, Machines and Control (PEMC) Research Institute
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for optimizing metals microstructures in-situ during the AM process as well as ex-situ during post-AM treatments and enable predictions of the microstructural evolution, and thus changes in properties, while AM
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conceptual framework linking nanoscale features to macroscopic adsorption efficiency. Generate and curate high-quality datasets to support data-driven materials optimization and future integration with AI
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on generating new knowledge for optimizing biological conversion of carbon dioxide to acetic acid in close collaboration with an industrial end-user of the developed technology. Responsibilities and tasks Your
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will focus on building understanding of the interplay between the upstream process parameters and process performance to develop approaches and strategies to optimize efficiency and robustness and allow
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the applicant: - For the dissertation and the subject relevant knowledge and skills, for example demonstrated strong background knowledge at advanced level especially related to automatic control, optimization