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on developing, implementing, and applying tensor-network and related quantum-dynamics methods. Applicants may indicate a preference for one project or ask to be considered for both. The start date is flexible
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: Ph.D. in Physics, Computer Science or closely related areas Preferred: Demonstrated strength in one or more of the following. - Quantum many-body numerics: tensor-network methods, variational Monte Carlo
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on computational many-body techniques, particularly Density Matrix Renormalization Group, Tensor Network, Exact Diagonalization and Monte Carlo simulations are strongly encouraged to apply. To apply, candidates
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, computer science, or engineering within the past 5 years. Previous theoretical and/or computational research experience in tensor networks, Monte Carlo, machine learning or a related field Proficiency in quantum
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one of the following areas: - Methodology development in wavefunction-based electronic structure methods, quantum Monte Carlo, tensor networks, or quantum embedding methods
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the computational foundations of probabilistic programming, such as automatic differentiation, tensor libraries (PyTensor, JAX), gradient-based samplers, or model transpilation and compilation. Experience with
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quantum magnetism and strongly correlated systems, as well as classical methods such as exact diagonalization, tensor networks or DMRG, and quantum Monte Carlo. Familiarity with inelastic neutron scattering
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structure methods, quantum Monte Carlo, tensor networks, or quantum embedding methods, etc. - ML-augmented numerical method development. - High-performance computing (HPC
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field theory, semi-classical methods in quantum many body dynamics, tensor networks and GPU-accelerated quantum evolution. Our work is concept- rather than method-centric. Candidates with backgrounds
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Requisition Id 16262 Overview: We are seeking a postdoctoral researcher to work at the intersection of tensor networks, quantum algorithms, scientific computing, topological physics, and quantum