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Offer Description Mission: Develop and apply advanced Quantum Monte Carlo techniques to investigate the ground state properties and phase transitions in two-component bosonic gases with coherent coupling
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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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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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(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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models that are incomplete and data that involve errors. For such challenges, Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard. For addressing high dimensional parameter
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interactions using Monte Carlo codes (MCNP, PHITS). Designing and performing numerical calculations of radiation shielding using Monte Carlo codes. Designing and performing Monte Carlo calculations
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, or computer science. Core competencies: solid background in quantum many-body physics strong programming skills (Python required, Rust a plus) experience with tensor networks, variational Monte-Carlo, machine learning
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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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spectrophotometric and time-resolved optical measurements, Monte Carlo simulations, and numerical solutions of Maxwell's equations. The expected outcomes will advance the fundamental understanding of radiative
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scan. The project will involve Monte Carlo simulations and hands-on experimental studies with a range of phantoms using the TBP scanners at King’s College London (KCL) to fully characterise