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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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Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be considered. This position is offered through the research funds of Mostafa
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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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About the Role The position is funded through the EPSRC project “Zeros, Algorithms, and Correlation for graph polynomials”. We study various combinatorially defined polynomials such as the
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, graph theory and more to characterize biomolecular systems. The project is predominantly an applied project focusing on protein characterization; however, there is a possibility for method development
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | about 1 month ago
. Project Description The successful candidate will work on the development of a Gaussian Process regression framework on graphs and its integration into: the Numerical Information Field Theory (NIFTy
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computational models; (ii) optimization and related graph-theoretic methods, using polyhedral geometry and algorithmic optimization (e.g., vertex/facet–based techniques) to formulate and solve core computational
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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