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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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features such as twinning, branching, and chirality. Research techniques primarily involve the use and further development of a kinetic Monte Carlo simulation framework, coded in C++. Other supportive
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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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electrocatalysis: The research involves the application and development of ab initio methods, molecular dynamics and kinetic Monte Carlo simulations to model electrocatalytic oxidation and reduction reactions