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on the development of continuum and discrete, stochastic mechanical models of ordered cellular structures and understanding the role of order in pattern formation. The project is in close collaboration with
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to enable scalability. The work will draw on concepts from dynamical systems, stochastic processes, and stochastic differential equations (SDEs), including nonequilibrium systems, to model cellular behavior
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candidate will have a strong foundation in statistical mechanics, stochastic processes, dynamical systems, complex systems, or related quantitative approaches; Experience in developing and implementing
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modeling, computational modeling, quantitative biology, dynamical systems, stochastic processes, complex systems, or related quantitative approaches. Candidates should have experience developing
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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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, Geosciences, Physics or Mathematics Knowledge of hydrological and meteorological processes and flood risk concepts Experience in statistics, particularly extreme value statistics, and stochastic simulation
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probability and approximation theory, high-dimensional stochastic dynamical systems (molecular dynamics in particular), model reduction, control. A strong background in analysis, probability, and at least one
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Independent research and publication activities in the field of mathematical optimization with focus on nonsmooth optimization, stochastic optimization, or optimal control of partial differential equations
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: Experience with mathematical, computational, or statistical modeling Experience developing simulation models, agent-based models, network models, stochastic systems, or dynamical systems Experience with R
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energy supply systems, multi-objective and stochastic optimization, advanced statistical analysis, and data visualization. This position offers the opportunity to work with a multidisciplinary team of