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variants of importance sampling. We will connect these methods to modern formulations of Monte Carlo algorithms to improve their accuracy, scalability, and overall computational cost. The methodology so
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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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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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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
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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