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the other is on a special grant on tensor networks and quantum computing for high-energy physics. Candidates interested in these and related topics are particularly welcome to apply. Successful candidates
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, lead large-scale benchmarking across the full stack, and develop scalable classical simulations (e.g., tensor networks)—including performance bounds beyond brute-force classical simulability. This role
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the full stack, and develop scalable classical simulations (e.g., tensor networks)--including performance bounds beyond brute-force classical simulability. This role is deeply collaborative with the Advanced
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Monte Carlo, neural quantum states, tensor networks, machine learning and data science, dynamical mean field theory, diagrammatic Monte Carlo, etc.) Key Responsibilities Conduct independent and
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, theoretically grounded, and explainable. The research sits at the crossroads of quantum information science, tensor network theory, and generative modeling (transformers, diffusion models, etc.). Key research
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on developing tensor network methods and other advanced computational approaches to explore novel quantum states and correlation effects in many-body systems, including but not limited to frustrated magnetism
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algebras, tensor categories, lattice models of statistical physics, conformally invariant random processes, formalization of mathematics (preferably in Lean). The working language of the group is English
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background in mathematics. We particularly value experience in the following topics: vertex operator algebras, infinite dimensional Lie algebras, tensor categories, lattice models of statistical physics
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simulation tools for correlated attosecond electron dynamics in molecules." The position will involve developing new methods related to tensor network states (e.g. TD-DMRG, MCTDH) to simulate correlated
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/quantum-circuit techniques and/or approximate tensor-network methods. Analytical skills with integrability, random-matrix theory, dual-unitary circuits, hydrodynamics, or field-theoretic methods are also