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Field
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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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(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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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
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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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potentials (DeePMD-kit, NequIP/Allegro, or similar). Familiarity with cluster-expansion/Monte Carlo methods, thermodynamic integration, or elastic-constant calculations. Knowledge of high-pressure mineral
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. • Direct experience of using Monte-Carlo-based simulation and optimisation tools. • A proven track record of high-quality research. Candidates are expected to demonstrate: • Ability to work as part of
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. • Direct experience of using Monte-Carlo-based simulation and optimisation tools. • A proven track record of high-quality research. Candidates are expected to demonstrate: • Ability to work as part of