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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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geometry, affine algebraic geometry Lena Bunina Model theory, AI in mathematics, algebraic groupsmodel theory of groups, Chevalley groups, applications of AI to mathematics Naomi Feldheim Probability
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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 considered
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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
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, computational fluid dynamics and material science, dynamical systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
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Shai Evra Graph theory, representation theory, number theory Adi Glucksam Complex analysis, potential theory, and dynamics Or Hershkovits Geometric analysis Mike Hochman Dynamical systems
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Quantum computing and Graph theory In this role, you will be responsible for conducting research on graph theoretic approaches to design quantum photonic experiments. Additionally, the position involves
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structural and algorithmic graph theory. The purpose of the role is to contribute to the project “Algorithmic meta-classifications for graph containment”, working with Professor Matthew Johnson, Dr Barnaby
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quantum information theory, algebraic topology, polyhedral combinatorics, graph theory, and optimization are strongly encouraged to apply. For more details on the project, visit open positions page . To be