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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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chains, random graphs and trees, random matrix theory, stochastic and Lévy processes in infinite-dimensional spaces, free probability, random sphere packings in high dimensions. About the role You will
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equations, non-linear analysis, graph theory, combinatorial optimization using metric-type structures, algebraic structures in analysis and topology, statistics and probability theory, application of data
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English. Technical Skills : Programming in Python; Git; JavaScript; HTML/CSS Simple full stack web technologies HTTP requests and REST APIs; XML tools: XSLT, XQuery, Xpath Graph theory & community detection
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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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Group (ECOPRO) Fellowship Type: Postdoctoral Fellowship Location: Daejeon, Daejeon 34126, Korea, The Republic of [map ] Subject Areas: Extremal and Probabilistic Combinatorics, Graph Theory, discrete
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/ Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date machine learning libraries Excellent written and verbal communication skills Track record of publishing
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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degrees through the doctoral level. More than 20 percent of its 25,000 students are enrolled in graduate course work, studying in disciplines ranging from atomic physics and graph theory to medieval
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than 20 percent of its 25,000 students are enrolled in graduate course work, studying in disciplines ranging from atomic physics and graph theory to medieval literature and blind rehabilitation. Of 101