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About the Atmospheric Research group of the Earth Sciences department Our research group is a nice team of PhD students, postdocs, and academic staff working on greenhouse gases, supporting reactive gases, and aerosols. We develop atmospheric models operating at scales ranging from local,...
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the core [2-5]. Contrary to the geomagnetic inverse problem for which there exists an intercomparison in the context of the IGRF [6], and to the numerical simulation of the geodynamo for which benchmarks
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signal processing schemes using machine learning methods and knowledge of inverse scattering methods (nonlinear Fourier transform). About us: AiPT is one of the world’s leading photonics research centres
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to develop specific surrogate models for BUQ. Surrogate models of the underlying PDEs in the Bayesian Inverse problem (or BUQ) are sough, as Physics informed interpolators, multiscale models, PINNs etc
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. Desirable extras include experience with wind‑energy structures, machine‑learning/inverse‑problem techniques and prior industrial collaboration. Further details about the role requirements are available in
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of Python programming and a deep learning framework, preferably PyTorch. Solid knowledge of image processing, inverse problems, and machine learning. Significant research experience, demonstrated by quality
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-Waals heterostructures. The research scope of the Research Unit also includes spin-wave physics, inverse-design magnonics, spintronics, millikelvin-temperature quantum physics, unconventional computing
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processing, inverse problems and data science with emphasis on analysis, optimization, numerics and algorithmic solution Collaboration in interdisciplinary cooperation projects and third-party funded projects
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Your job As a Postdoctoral researcher, you will contribute to two international ESA projects: PIRAMID(Progress in the Interpretation of Remotely Sensed Atmospheric Climate Products using Modelling
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. The specific topic of the project falls in the intersection of statistical signal processing and applied mathematics and is in particular concerned with optimal transport for inverse problems. Optimal