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account for molecular interactions in electrolyte solutions, including complex chemical reactions; (2) to enable real-time CO2 storage process simulations by optimizing the framework’s computational
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bioprocess optimization. The successful candidate will contribute to the development of sustainable bioconversion platforms for the transformation of biogas-derived carbon into amino acid-enriched biomass
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optimization, using machine learning and advanced metaheuristics. Furthermore, you should (together with the team) participate in development of solvers for stochastic optimization problems and test the methods
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protype within the duration of the Ph.D. project. The successful candidate will develop physical models of the system being built with the aim of predicting and optimizing its performance. In addition
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on holistic understanding from regional net-zero. The PhD project is part of the Regional Energy, Carbon and Land Management initiative, focusing on optimizing energy infrastructure, land use and carbon flows
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, international group of 13 researchers from 8 countries, with expertise across energy systems and markets, optimization, control, game theory, and machine learning. Interdisciplinary by design: Work at the
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, programming, and computer simulations. The focus of the project (funded by the Danish Council for Independent Research) is to explore the interaction between optimally controlled coherent (laser) light fields
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of Bayesian optimisation Experience of numerical computing for optimal control, dynamical systems, Bayesian inference and Bayesian optimisation. Experience of running controlled fermentation experiments
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or BSc + one year of Master Studies in mathematical engineering, mathematics, computer science, electrical engineering or similar. Solid mathematical and analytical skills, including optimization and/or