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be done via computer simulations, including Monte Carlo and molecular dynamics, combined with the use of statistical mechanics to predict e.g. phase transitions, nucleation rates, etc. The work will be
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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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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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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