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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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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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-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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coupled nuclear engineering problems, using techniques such as (but not limited to) molecular dynamics, computational fluid dynamics, activation decay codes, kinetic Monte Carlo codes, particle transport
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between theoretical and computational high-energy physics. The research contributes to the world-leading PYTHIA Monte Carlo Event Generator, which serves as the baseline for the majority of experimental