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Field
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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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computational tools for predicting satellite features in XPS spectra of 2D framework materials. Your work will be based on the GW approximation within Green’s function theory. While the GW method reliably
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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: Berne, Bern 3012, Switzerland [map ] Subject Areas: Physics / Nuclear Physics High Energy Physics / Phenomenology High-Energy Theory / Particle Physics Appl Deadline: (posted 2025/04/02, listed until
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defiance by centering on visual analysis grounds aspirational urbanism, rescaling the object of urban theory towards everyday micro-practices.
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-based symbolic machine learning. The aim is to give the industrial widely used NewWave theory (a theory that predicts the linear shape of extreme waves) a modern twist to account for the important
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powerful ideas and tools at the intersection of topological band theory, symmetry analysis, and photonics. You will work on developing and applying these ideas to discover new topological phenomena, design
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focus more on model development, robustness, and long-term reliability. What you can expect Modelling. Apply probability theory, statistical analysis, and machine learning techniques to build robust
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, hierarchical and composable analysis and design is needed. The project will leverage tools from contract-based design theory to formalise such security and performance guarantees at multiple resolutions