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/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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area of Drosophila neural development: How are stochastic choices made in sensory neuronal development coordinated with the deterministic generation of neuronal diversity in the synaptic targets
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techniques may be employed to support the modeling of uncertainty, along with the formulation and resolution of planning problems using stochastic optimization methods. Key Responsibilities The selected
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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Boltzmann and Landau kinetic theory, dynamic mean-field theory and the theory of stochastic processes including Fokker-Planck Equations and path integral techniques. However, applicants from other areas
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The Weierstrass Institute for Applied Analysis and Stochastics (WIAS) is an institute of the Forschungsverbund Berlin e.V. (FVB). The FVB comprises seven non-university research institutes in Berlin
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methods, stochastic control processes, dynamic programming, deep reinforcement learning. Strong track record in scientific contributions supported by peer-reviewed publications. Strong programming skills
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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large