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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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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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: extremal graph theory, Ramsey theory, probabilistic combinatorics. • Candidates should have (or be near completion of) a PhD in mathematics. • Candidates should have a strong research record in Combinatorics
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Theory, Queuing Theory, Age of Information), Network Calculus, Graph Theory, Convex and Non-convex Optimization, Approximation Algorithms. An excellent Master’s degree in Computer Science, Engineering
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main areas of research are machine learning, distributed systems, and the theory of networks. Within these three areas, we are currently working on several projects: graph neural networks, natural
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Theory, Queuing Theory, Age of Information), Network Calculus, Graph Theory, Convex and Non-convex Optimization, Approximation Algorithms. An excellent Master’s degree in Computer Science, Engineering