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to automate, accelerate and improve decision making. Analysing graph data requires solving problems at the boundaries of machine learning, graph theory, and algorithmics. Your future tasks: You actively
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. Analysing graph data requires solving problems at the boundaries of machine learning, graph theory, and algorithmics. Your future tasks: You actively participate in research, teaching & administration, which
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at the boundaries of machine learning, graph theory, and algorithmics. Your future tasks: You actively participate in research, teaching & administration, which means: You are involved in research projects in
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to automate, accelerate and improve decision making. Analysing graph data requires solving problems at the boundaries of machine learning, graph theory, and algorithmics. Your future tasks: You actively
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). "Statistical field theory applied to complex networks” "Quantum geometrogenesis – Graph theoretic approaches to building spacetime” web page For further details or to discuss alternative project arrangements
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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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with multitarget estimation for direction-of-arrival (DOA) detection and tracking in radar theory [12]. Graphs are a powerful data structure to represent relational data and are widely used to describe
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Vacancies 2x PhD positions in the Mathematical Foundations of Machine Learning on Graphs and Networks Key takeaways The Discrete Mathematics and Mathematical Programming (DMMP) group
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Umeå University is offering a PhD position in Computing Science with a focus on machine learning for graph
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provably powerful learning models for graphs will require new mathematical machinery. LOGSMS will combine diverse tools from discrete mathematics, learning theory and machine learning, thus facilitating