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Field
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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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Omer Ben-Neria Logic, set theory Shai Evra Graph theory, representation theory, number theory Adi Glucksam Complex analysis, potential theory, and dynamics Or Hershkovits Geometric analysis
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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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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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Zerbib. This includes graph theory, discrete geometry, topological combinatorics, extremal combinatorics, and flag algebras. The position has a 2-1 teaching load and a requirement to be involved with
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inequalities and Sobolev-type spaces (with Hytönen and/or Korte), 3. Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic
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-doctoral associate to work on one or more of the following topics: Mathematical Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be
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architecture and design for complex socio-technical systems Graph theory, network science, and knowledge representation Agent-based and simulation modeling AI/ML, foundation models, causal inference, and
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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 theoretic
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and Sobolev-type spaces (with Hytönen and/or Korte), Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic game