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parameters by trial-and-error, leading to a time consuming sub-optimal selection. In the domain of high precision machining, tools are prematurely discarded to avoid the risk of costly non-conformities
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machine learning for safe and optimal control of cyber-physical systems. The projects are expected to be funded by the VILLUM INVESTIGATOR project S4OS (“Scalable analysis and synthesis of safe, secure and
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industry’s biggest challenges: Closed-Loop Design and Optimization of Biologics. The research program will build on the recent advances in protein design, automation, and multi-parameter optimization and
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are carried out in direct collaboration with industry to a great extent. The position is offered in relation to the research program "Power Electronic Control Reliability and System Optimization" and the PhD
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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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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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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key