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Field
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of random variables, simulating solutions of stochastic differential equations, variance reduction methods, multi-level sampling, least square Monte Carlo, Markov chain Monte Carlo, and solving partial
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critical to ensuring the longevity and safety of fusion reactors. This PhD project focuses on developing an integrated framework that combines cutting-edge computational models, including Monte Carlo
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for this role include: Conduct research in the area of High Dimensional Approximation, Uncertainty Quantification, Deep Learning, and Quasi-Monte Carlo Methods independently and as part of a team, including
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condensed matter and or materials physics, or related field. Experience with computational many-body physics is desired, including techniques such as the dynamical mean-field theory, quantum Monte-Carlo
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(CPU/GPU), numerical modeling/Monte Carlo simulations are an asset Visualisation skills are an asset Careful way of working, checking of results Candidates can have an M.Sc. degree in STEM, or a Ph.D
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collaborating with industry partners on a project aimed at developing kinetic Monte Carlo simulations to model epitaxial growth processes. The goal is to control and optimise the growth of nanoscale structures
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System Models, or related simulation tools. Additional knowledge of statistical methods, Monte Carlo techniques, or ensemble approaches is highly valued. The post is advertised as full-time. We are open to
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-atomic potentials (e.g. pro-fit, MLIP-3). Knowledge of meso-scale models such as cluster dynamics (e.g. Xolotl, Centipede), object-kinetic Monte Carlo or similar. Proven commitment to proactively keeping
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groups in theoretical and experimental particle physics and astrophysics. The successful candidate will conduct research on precision calculations for high-energy collider experiments and Monte Carlo
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with computational many-body physics is desired, including techniques such as the dynamical mean-field theory, quantum Monte-Carlo approaches, diagonalization approaches, etc. Strong candidates with