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About the project: Advanced Monte Carlo methods for glassy dynamics and complex materials Supervisor: Dr Michael Faulkner, University of Warwick Glasses are materials that combine macroscopic solid
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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
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recent years in conjunction with Variational Monte Carlo methods and efficient optimization techniques to obtain excellent representations of the ground state of many-body quantum systems
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particular focus on Small Modular Reactors and spent nuclear fuel management. The research will examine radiation- and high-temperature-resistant high-entropy alloys using ab initio, many-body, and Monte Carlo
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theory, Bayesian inference, Monte Carlo simulation, and statistical analysis of subjective data. Data science and machine learning - big data analytics, surrogate modelling, digital twin development, and
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greenhouse relevant molecules • Use particle-based Monte Carlo codes and couple them with atmospheric chemistry models • Quantify the annual production of greenhouse gases induced by gamma-ray glows
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cruising altitudes • Model the production of secondary energetic particles (positrons, neutrons, protons) in the atmosphere • Use particle Monte Carlo codes and the Geant4 toolkit to study interactions
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. Provide insight on the appropriate use of Monte Carlo, deterministic, and AI-accelerated approaches for HTGR design, safety assessment, and operational analysis. Develop and validate a multiscale thermal
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of particle detectors. The candidate is expected to engage in planning for deployment of these tools in view of software releases, Monte Carlo campaigns, and data taking runs of the detector. Additionally
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or Monte Carlo methods - Hands-on experience with experimental work - Knowledge of the german language is desired but not mandatory Application process You should send a motivational statement, a curriculum