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a doctorate. We are looking for: candidates with a Master’s degree in mathematics or a closely related field and with a strong background in probability theory. Prior knowledge in spatial stochastic
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a doctorate. We are looking for: candidates with a Master’s degree in mathematics or a closely related field and with a strong background in probability theory. Prior knowledge in spatial stochastic
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to model and analyse the intrinsic complexities of these systems. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains
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, like Kron reductions from graph theory. The aim is to find an approximation of the system of manageable scale while preserving its essential properties. One promising method for power system model
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. Your qualifications An excellent PhD degree either in Computer Science, Physics, Mathematics or related fields, ideally with a background in quantum theory, quantum computing or quantum machine learning