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Field
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computation, probabilistic machine learning, latent-variable models, unsupervised learning, or matrix and tensor factorization is an advantage. Experience with computational methods for large or high
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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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concepts such as data parallelism, tensor parallelism, pipeline parallelism, checkpointing, and GPU communication. Experience with frameworks such as veRL, slime, Megatron-LM, DeepSpeed, TRL, vLLM, SGLang
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computation, probabilistic machine learning, latent-variable models, unsupervised learning, or matrix and tensor factorization is an advantage. Experience with computational methods for large or high
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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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using matrix product states (MPS) and broader tensor-network techniques (MPS/TEBD/DMRG, PEPS where relevant). This benchmarking pipeline is essential: it quantifies accuracy, identifies the regimes where
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characterization experiments on mineralized biological tissue, in particular small-angle X-ray scattering tensor tomography Publish results in scientific journals and present findings at international conferences
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will have training and experience in the analysis of human brain imaging data, including functional MRI (fMRI) and diffusion tensor imaging (DTI), and a strong interest in translational neuroscience. Our
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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