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Field
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of mathematical modeling, with a particular focus on stochastic modeling, optimization and more recently machine learning. Indeed, over the last years, the team’s activity has been marked by a strong shift toward
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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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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models
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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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concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical
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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