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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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reinforcement learning (RL), Bayes adaptive RL, and planning with novel variants of Monte Carlo tree search. You should possess a relevant PhD/DPhil or be near completion together with relevant experience
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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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(for example, efficient sampling from the Gibbs measure, Markov Chain Monte Carlo methods, etc.). Experience in distributed or parallel computing is a plus, but is not a requirement. The position does not
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effects on astronauts. Detailed analyses of such effects, and possible ways to shield and mitigate against them, are typically conducted by using environmental prediction in combination with Monte Carlo
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: exact diagonalization, Monte Carlo, and where appropriate, tensor network techniques. The postholder should also have a broad familiarity with the theory of strongly correlated systems and excellent oral
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apply expertise in techniques such as reflectance spectroscopy and statistical modeling approaches, including Markov chain Monte Carlo methods, to support scientific investigations of planetary
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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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. A well-qualified candidate for this position will also possess: Familiarity with QCD theory, heavy ion physics, Monte Carlo event generation techniques. Experience using and developing JETSCAPE event
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Monte Carlo simulated data, but there is also the possibility of working with real open data from the ATLAS and/or CMS experiments. The methods are general and applicable well beyond particle physics