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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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transport modeling and machine protection strategies for the EIC accelerator complex. This position will focus on Monte Carlo simulations to characterize the radiation environment resulting from beam losses
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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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, geometric modelling, acoustic signal propagation, Monte Carlo simulation methods, decision theory, uncertainty quantification, machine learning. Applications and areas of key innovation Image analysis
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experiments, including setup of detectors, electronics, and data acquisition systems. Experience analyzing complex experimental and Monte-Carlo simulated data to optimize the analysis of raw detector data in
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, sensing techniques, optimisation theory and algorithms, multi-modal data processing, high-performance computing, mathematical image analysis, geometric modelling, acoustic signal propagation, Monte Carlo
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financial dynamics Apply machine learning and Monte Carlo techniques to simulate complex decision scenarios Contribute to a growing, interdisciplinary field that redefines biodiversity through the lens
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, LROC, ROC, Image perception, mathematical/computational observer models. Both experimental and computational positions available. Skills preferred include any of these: Monte Carlo simulations, benchtop