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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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of the PhD work and identify isotopes of interests that could be studied, the PhD student will need to learn how to use a Monte Carlo code. This tool will enable the candidate to perform simulations
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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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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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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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. 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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with multi-target tracking (MTT) and RFS-based filters (e.g., PHD, JPDA). Experience in orbit determination, SSA/SDA, or relative navigation. Experience with Monte Carlo validation, uncertainty