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well as knowledge to support activities related to optimization methods across integrated energy systems and energy markets. The position is offered in relation to the research group iGRIDS- Intelligent Energy
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expected evolution of these critical properties as the basis for safe reinforcement learning (RL) for on-line optimal control”. In particular, the stipend will investigate enhancement of RL controllers in
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the development of innovative and energy-efficient thermochemical processes that enable a sustainable, de-fossilized carbon economy. We explore renewable carbon sources and investigate optimal conversion pathways
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projects in corporation with industry partners. Teaching will primarily be in control theory, hydraulics, dynamic systems, and optimization, but also in other study groups at the University. Furthermore
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materials, and will develop AI-driven Bayesian decision modelling for the optimization of experiments. Further, the candidate will support the development of safety formats and calibration of safety factors
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modeling, optimization techniques, hybrid testing and digital twins. Furthermore, the position aims at incorporating machine learning to drive innovation in the areas. Possible applications are within