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Field
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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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on theoretical and computational aspects of quantum many-body systems, including Tensor Networks, Neural Quantum States, Stabilizer formalism, Complexity measures such as entanglement and quantum magic, quantum
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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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tensors and local plastic strains (including operating slip systems) from Lab-4DµXRD data, in collaboration with the DTU Lab4DMade team and the Danish company Xnovo Technology ApS. Performing in situ Lab
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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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tensors and Taylor models as tools to map neural systems onto mathematically well-understood objects. Pioneering the field, the ACT has developed several innovations, including deep learning for guidance
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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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, or tensor-network simulations of dynamics. The Nonequilibrium Statistical Physics and Mathematical Physics group offers a vibrant and collaborative research environment. Current members include Tomaž Prosen
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are especially encouraged: (1) statistical mechanics, condensed-matter theory, quantum field theory; (2) tensor networks, quantum information, quantum algorithms; (3) machine learning, generative models, large
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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