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critical to ensuring the longevity and safety of fusion reactors. This PhD project focuses on developing an integrated framework that combines cutting-edge computational models, including Monte Carlo
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profile: Must-have: MSc or equivalent degree in computational simulations and related fields Theoretical knowledge of Molecular Dynamics and/or Monte Carlo Good English language – spoken and written Nice
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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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qualifications Documented experience with data analysis and programming (e.g., Matlab, Python or R). Experience of risk assessment and/or decision analysis Experience of probabilistic methods such as Monte Carlo
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focusing on: Quantum mechanical calculations using density functional theory. Mean-field modeling and Monte Carlo simulations for reaction kinetics. Theoretical spectroscopy By combining quantum mechanical
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use Systems Biology methods to formulate a set of ordinary differential equations describing how genes regulate each other across the different organelles. Another approach is to use Monte Carlo
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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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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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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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the different organelles. Another approach is to use Monte Carlo simulations to explore which gene regulatory network architectures are necessary for robust regulation and effective communication between