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Requisition Id 15472 Overview: We are seeking a Postdoctoral Research Associate who will contribute to the development and implementation of novel tensor network algorithms and their combination
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Description Two postdoc positions are available as part of the Lumina quaeruntur project "Models and filtrations in tensor triangular geometry" at the Department of Algebra, Geometry and Mathematical Physics
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physics is especially interesting due to the presence of exotic excitations, potentially non-Abelian. The TensQHE project aims to develop modern numerical tools based on tensor networks, within an open
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Daegu-Gyeongbuk Institute of Science and Technology | Republic, Kansas | United States | about 24 hours ago
Daegu-Gyeongbuk Institute of Science and Technology, Global Basic Research Lab - Algebra and Geometry of Spaces of Tensors and Applications Position ID: 3672-PD [#27657] Position Title: Position
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relevant to setting a roadmap for ongoing experiments, as well as recently developed applications of tensor network techniques to large-scale partial differential equations. We are advertising two positions
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on problems of interest to the Simons Collaboration on Probabilistic Paths to Quantum Field Theory, in particular on the tensor network renormalization group for statistical physics lattice models. We welcome
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Institut des Hautes Études Scientifiques | Bures sur Yvette, le de France | France | about 11 hours ago
on problems of interest to the Simons Collaboration on Probabilistic Paths to Quantum Field Theory, in particular on the tensor network renormalization group for statistical physics lattice models. We welcome
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reinforcement learning and/or tensor networks are preferred. Other responsibilities include communicating results in journal publications and conferences as well as mentoring junior members of the group
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of quantum information science, tensor network theory, and generative modeling (transformers, diffusion models, etc.). - Conduct cutting-edge research at the intersection of quantum information, machine
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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