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Doctoral Researcher in statistical signal processing. The Structured and Stochastic Modeling Group, headed by Prof. Filip Elvander, conducts research in statistical signal processing, ranging from
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, but exceptional applicants can be considered. All areas related to systems modeling, decision support, stochastic methods, and computational modeling with applications to environmental or water
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of policy modules (including several tax calculators), and a large-scale stochastic macroeconomic overlapping generations (OLG) lifecycle model. An extensive code base, mostly in Python, underlies
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(PHM) for aerospace operations and maintenance, with strong expertise in sensor fusion, stochastic modeling, and machine learning. Your role: Design, build, and commission a laboratory test bench for
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equations, stochastics, surrogate modeling; as well as data availability, security-related issues, and ethics with AI in mechanics. Tasks: Research: Internationally competitive research activities related
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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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asymptotic analysis of stochastic processes Impact: Faster detection of anomalies and reliable uncertainty quantification Job Description As a PhD candidate in Mathematical Statistics, you will develop novel
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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- related field by the time of the appointment. The second position is in applied mathematics or statistics and is focused on the areas of dynamics, stochastic processes, complex systems, or statistics
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Number Theory, (2) Geometry and Topology, (3) Probability and Stochastic Analysis, (4) Discrete and Geometric Analysis, (5) Statistics and Data Science, and (6) Partial Differential Equations and Modelling