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learning, or related quantitative field. • Proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in
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Description Open one or two year Postdocs positions on Surrogate Models for Uncertainty Quantification (UQ), at the Centro de Investigación en Matemáticas (CIMAT-SECIHTI, México). We are a UQ research group
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collaboration with business and industry, and numerous researchers from the department have established companies to develop new medicinal treatments founded in professional scientific basic research. You can
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candidate will join Dr. Siddharth Saksena’s research group, which focuses on advancing hydrologic modeling, flood forecasting, and hydroinformatics through the integration of artificial intelligence, physics
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from advanced centrifuge tests with numerical simulations. We are looking for candidates with a PhD in geotechnical engineering and proven experience in numerical modeling of offshore foundation systems
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08.09.2025, Wissenschaftliches Personal with a strong connection to partial differential equations (numerics / analysis / modeling) (100%, TV-L) is available starting immediately. The position in
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have a strong background in theoretical modeling and/or numerical simulations of quantum matter, including their electronic structure, electron-electron correlations, topology, and transport/optical
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/or observationally motivated model-building for SMBH feeding and feedback. The group integrates observations, theory, data analysis, algorithm development, model-building, and numerical simulations
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supervision of Prof. Yingda Cheng on computational methods and modeling for kinetic equations. The research conducted will involve development of numerical methods, development and analysis of reduced order
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; Couple the model to a 1D physical model (GOTM) through the FABM framework; Validate the coupled model using published and newly gathered observational data; Conduct numerical experiments to understand