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Field
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. Methodological Areas of Interest Applicants with experience in the following areas are especially encouraged to apply: Optimization (deterministic, stochastic, robust, reinforcement learning–based) Systems
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Amir Probability and group theoryRandom walks, probability and geometry on groups, harmonic functions, opinion dynamics and other stochastic processes on graphs Gil Ariel Bacterial swarming, collective
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learning - deep neural network, recurrent neural network, LSTM Optimization theory Stochastic geometry Strong programming skills in at least one of the following: MATLAB, Python, JULIA or C++. Highly
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encouraged to apply: • Optimization (deterministic, stochastic, robust, reinforcement learning–based) • Systems architecture and design for complex socio-technical systems • Graph theory, network science, and
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degree must be conferred by time of appointment. Strong background in one or more of the following areas: Optimal control, MPC, RL, or reachability analysis. Uncertainty quantification, stochastic modeling
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of Chemistry, and the group of Prof. Hobolth at the Department of Mathematics. A second postdoc with expertise in stochastic processes and statistical methods will be part of the project and you are expected
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of Chemistry, and the group of Prof. Hobolth at the Department of Mathematics. A second postdoc with expertise in stochastic processes and statistical methods will be part of the project and you are expected
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; numerical analysis; programming; optimization; stochastic analysis; systems theory. What you will do You are expected to develop your own ideas and communicate scientific results orally as well as in written
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to postdoctoral mentorship and supports independent career development for successful candidates. For Pre-Fire domain, we seek candidates with strong background in engineering risk, reliability, and stochastic
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differential equations, computational fluid dynamics and material science, dynamical systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology