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quantification and data science. Potential investigation areas: • Enhancing Monte Carlo and Markov Chain Monte Carlo (MCMC) with reinforcement learning. • Developing adaptive tuning and continual learning
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of a DG method for solving the hyperbolic BBGK equation has not yet been fully exploited by the rarefied gas dynamics community which continues to rely heavily on the costly direct simulation Monte Carlo
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the theory by putting it on a lattice. The resulting equations are solved numerically using specialized Monte Carlo techniques. There are many applications that require the solution of QCD so that the standard
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excite states in certain materials which can then be identified through inspection of the neutron energy spectra or emitted radiation. The project will initially involve simulation work using Monte-Carlo
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Materials 2024 (OMat24), or Open Catalyst 2022 (OC22), which will be used to refine the GenAI-proposed steps, and (iii) using the dataset generated by GenAI to parameterise kinetic Monte Carlo (KMC
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manufacture, to enable quantitative imaging. Your research will include a mix of computational and experimental work to develop and characterise these instruments. Monte Carlo simulations (using GEANT4) will
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physics or related discipline (assessed at: application/interview) Familiarity with the experimental techniques in particle physics (assessed at: application/interview) Experience in Monte Carlo modelling
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develop innovative techniques to accelerate computational methods in uncertainty quantification and data science. Potential investigation areas: • Enhancing Monte Carlo and Markov Chain Monte Carlo (MCMC