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Siri Pedersen 9th August 2026 Languages English Norsk Bokmål English English PhD Fellowship in Data-Driven Optimization of Multi-Energy Systems Apply for this job See advertisement Job description
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photovoltaic generation, hydrogen production, and storage alternatives with microgrid-driven power distribution. Through advanced modelling and optimization techniques, this research aims to identify optimum
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modelling and optimization techniques, this research aims to identify optimum design and operational strategies that enhance system flexibility, improve resilience to variable supply and demand, and minimize
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surgery. However, the growing use of TAVI has also increased the need for accurate methods to assess valve disease, identify patients who will benefit from intervention, determine the optimal timing
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, e.g. in Python, particularly for machine learning Experience with machine learning theory, evaluation metrics, algorithmic fairness, statistics, or optimization is an advantage Language requirement
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storage, demand‑side management, and hybrid PV–wind systems. Development of optimal strategies for increasing grid hosting capacity considering power quality, voltage stability, and losses. The research
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ecosystems where interconnected multi-agents interact strategically in dynamic and uncertain environments. While Artificial Intelligence (AI) optimizes predictions or policies, energy systems are inherently
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optimization strategies, including reinforcement learning (RL) and optimal control approaches for state preparation and measurement protocol design under hardware constraints. Develop open-source software and
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, e.g. in Python, particularly for machine learning Experience with machine learning theory, evaluation metrics, algorithmic fairness, statistics, or optimization is an advantage Language requirement
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optimization strategies, including reinforcement learning (RL) and optimal control approaches for state preparation and measurement protocol design under hardware constraints. Develop open-source software and