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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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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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. 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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++) and confidence working with numerical tools. Desirable: Familiarity with heat transfer/thermodynamics and/or finite‑element methods. Prior exposure to reactor physics (deterministic or Monte Carlo
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which can reveal previously unknown phase equilibria. In this project we will extend NS to incorporate collective Monte Carlo (MC) moves designed for flexible molecules, extending the applicability