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Field
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with recent AI tools, are changing how mathematics is done. We are opening a postdoctoral position to explore what formal verification and algorithm discovery can bring to numerical analysis. There is no fixed
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analysis, machine learning, mathematics of data scienceMathematical modeling and manifold learning of high-dimensional data geometry, with theoretical foundations and applications in evolutionary biology and
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modelling, numerical development and the analysis of physical phenomena. In particular, they will be involved in designing models suited to the systems under study, their numerical implementation, and the
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Position Description Position Description The research group of Professor Mikaela Iacobelli in the Department of Mathematics at ETH Zurich invites applications for a two-year postdoctoral position
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developing innovative probabilistic methods to tackle fundamental problems at the intersection of spectral geometry, mathematical physics, and number theory. By exploring the geometry and spatial features
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artificial intelligence. Potential research topics include the mathematical and statistical analysis of modern generative models, particularly diffusion- and flow-based models, as well as their applications
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Do you have a strong mathematical background in geometry or numerical analysis? Are you inspired by applications to engineering, physics, or industrial problems? Do you thrive in a multi
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-based environmental modeling. Strong experience with numerical ocean or environmental models, including model configuration, forcing, simulation, analysis, and interpretation of multidimensional model
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throughout their evolution. Our vision combines ideas from differential geometry, numerical analysis, scientific computing, dynamical systems, and applied mathematics to develop new mathematical frameworks
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. - Applied Mathematics and Statistics: Knowledge of statistical methods and algorithms for data analysis, including model fitting, regression, and sensitivity analysis. Expertise - Scientific Collaboration