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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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analysis related to sampling, optimisation and learning problems in high dimensions. Examples of current research topics include convergence analysis of Markov processes, efficient Monte Carlo methods, large
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financial dynamics Apply machine learning and Monte Carlo techniques to simulate complex decision scenarios Contribute to a growing, interdisciplinary field that redefines biodiversity through the lens
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description The candidate will work on problems at the intersection of mathematical statistics, machine learning, and generative modeling, particularly for sequential data arising in complex dynamical systems