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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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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
following areas: High-dimensional probability and concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations
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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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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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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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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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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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, 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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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