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-field theory or variational Monte Carlo Good communication skills (oral and written) in English Informal inquiries about the position can be sent to Jan Kuneš (kunes@physics.muni.cz ). We Offer Diverse
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of the following is considered an asset: Variational methods for many-body systems (e.g. MCTDH, DMRG, Quantum Monte Carlo). Exact diagonalization for lattice models. Tensor network methods (e.g. MPS, PEPS). Machine
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matter observatory. Main responsibilities The postdoctoral candidate is expected to focus on statistical data analysis including machine learning, Monte Carlo simulations, operations and calibration
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nuclear physics detectors. Experience analyzing data from high energy or nuclear physics experiments. Familiarity with Monte Carlo simulations. Familiarity with machine learning techniques. About the
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performing statistical analysis, developing statistical approaches, performing Monte Carlo simulations, processing field and remote sensing data, writing scientific articles in national and international
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: proficiency in Monte Carlo simulation codes, such as GEANT 4 or PENELOPE. Bayesian statistics. • Laser polarisation system: appetite for experimental physics, experience with lasers not required but would be a
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sensing. This requires performing statistical analysis, developing statistical approaches, performing Monte Carlo simulations, processing field and remote sensing data, writing scientific articles in
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such as proteins, antibodies, nucleic acid structures, and soft polymeric materials. Tasks include the design of accurate and efficient coarse-grained models, simulation using classical Monte Carlo and
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topics include other new quantum symmetry-breaking and topologicallyordered states, unconventional phase transitions, and application of newly developed methods, such as Diagrammatic Monte-Carlo
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efficient and theoretically well-founded Monte Carlo methods for sampling from the complex posterior distributions that arise. The project, funded by the Swedish Research Council, is open to students with