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, United States of America [map ] Subject Area: Numerical Methods for Nonlinear PDEs Appl Deadline: (posted 2025/08/27, listed until 2026/02/27) Position Description: Apply Position Description The Mathematics Department
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at the intersection of numerical linear algebra and advanced HPC. The candidate will join an international environment, with opportunities to collaborate with experts from the USA, and KAUST and publish in top-tier
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structural health monitoring of structures immersed in heavy fluids, by continuing to develop the “Modal Strain Energy” and “Matched Field Processing” methods for detecting and locating a potential defect in
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develop cutting-edge statistical models and computational methods to systematically extract knowledge of molecular interactions from single-cell multi-omic, multi-context data. As an integral part of our
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between research institutes and industry partners, combining experimental and numerical expertise. This position is part of the ANR DYNATERRE project, which focuses on the static and dynamic behavior
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and Ar-Ar geochronology, fission-track and (U-Th-Sm)/He thermochronology, vitrinite reflectance, and thermal history models. New relational data models data for incorporating methods such as include
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relevant to the project, such as physics. Fluency in English, both in oral and written forms, is mandatory. The candidate should have a strong interest in physics (especially optics), numerical methods, and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 3 hours ago
computational methods. These quantities feed into our group's thermodynamic and geophysical numerical models. In addition, we are interested in investigating the consequences of the presence of organic matter in
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 10 hours ago
must be able to use a combination of numerical techniques and analytic perturbation theory to model more accurately the ringdown phase of black hole mergers, determining the non-linear contributions
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deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in interpretable ML and mechanistic model discovery. Submit a