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probability, allow for the application of tools from probability theory to combinatorial problems and motivate the study of the typical properties of various combinatorial models, such as the Erdős–Rényi random
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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12 Sep 2025 Job Information Organisation/Company CNRS Department Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis Research Field Engineering Computer science Mathematics
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programs. Alternatively, Mathematics, Computer Science, Computer Engineering, Electrical Engineering, or a similar field; Strong mathematical background: basic knowledge of graph theory and excellent
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a similar field; Strong mathematical background: basic knowledge of graph theory and excellent background in linear algebra, finite fields and rings; Strong background in digital hardware design and
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, or a similar field; Strong mathematical background: basic knowledge of graph theory and excellent background in linear algebra, finite fields and rings; Strong background in digital hardware design and
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addition to CSK from existing KBs) in targeted avenues such as multipurpose robots, along with mathematical modeling and algorithmic insights Counterbalancing issues such as bias, overfitting, and inexplicable
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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