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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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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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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
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Biochemistry, Molecular/Cell Biology, and/or BioEngineering documented by peer-reviewed publications Skills in a broad range of methods in the fields of Biochemistry, Molecular/Cell Biology, and/or
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- Develop original numerical methods for facility simulation in presence of expansion waves - Demonstrate improved estimates of rate constants for two-temperature models - Contribute
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Experience in machine-learning modeling for solid mechanics applications Experience in the development and coupling of numerical methods for solid mechanics modeling Experience in digital rock technology
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challenging task. In most cases, only a sample of a network is observed. Therefore, network completion needs to be addressed. Matrix completion methods have proved to be efficient when reconstructing a non
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responsible for conducting numerical and theoretical studies of turbulent flows and their associated geometric structures, focusing on passive and active scalar fronts, multiphase mixing layers, and
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or BioEngineering (or equivalent) Solid background in Biochemistry, Molecular/Cell Biology, and/or BioEngineering documented by peer-reviewed publications Skills in a broad range of methods in the fields