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of the growing amount of structured data in all these areas to automate, accelerate and improve decision making. Analysing graph data requires solving problems at the boundaries of machine learning, graph theory
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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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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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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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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
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for the first four full-time equivalent years of your doctoral studies. You will have the opportunity to work with leading national and international researchers – experts in social network theory, qualitative