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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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objective is to surpass the current traditional thermodynamic and optimization approaches, which are constrained in design discovery capabilities and long-term TES performance evaluation. Through your
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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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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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interdisciplinary competences in: communication theory, networking, information theory, physics, mathematics, computer science, and statistics. This PhD project falls under Research Thrust RT3 on representation
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i.e. turning towards in-line production and quality control will help save natural resources as well as reduce waste material and energy consumption. Formulation and test methods using mathematical
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or passive components into organic substrates; has experiences in magnetic components design, optimization and integration; is familiar with the simulation tools such as Ansys (Maxwell, Q3D, Icepak), LTSpice
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individual with a MSc degree in computer science, mathematics, chemistry, computational biology or a related subject. The ideal candidate has familiarity with one or more of the following areas: algorithmics
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individual with a MSc degree in computer science, mathematics, chemistry, computational biology or a related subject. The ideal candidate has familiarity with one or more of the following areas: algorithmics