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Discrete Mathematics and conduct research related to problems in Combinatorics, Graph theory and aspects of the Constraint Satisfaction Problem (CSP) with emphasis on topological methods. Further
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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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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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are passionate about any or all of the following: Data Science, Computational Social Science, Behavioral Economics, Human-Bot interaction, Experimental Research, Game Theory, and Artificial Intelligence. Some of
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and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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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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include statistical analysis, data management and collection, causal inference, network analysis, graph theory, visualizations, and online tool development. Experience in conducting online controlled
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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) Experience with modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow) Background in at least one of the following: graph learning, scientific computing, surrogate modeling, or ML theory Interest in
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relevant expertise: A PhD in Computer Science or a closely related field, with specialization in Quantum computing and Graph theory In this role, you will be responsible for conducting research on graph