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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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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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platforms can unify production environments, enabling predictive maintenance and data-driven optimization through centralized data platform architectures. Your research will focus on addressing current
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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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are looking for a passionate PhD candidate in Thermal Energy Systems with strong programming, optimization, and dynamic analysis of energy systems. This position is on the Horizon Europe-funded project