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Field
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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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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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learning and deep learning methods to analyze multi-omics data (genetic, epigenetic, transcriptomic, imaging, single-cell genomics and spatial omics data) with the goal of understanding the underlying
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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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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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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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, PETS has developed into a major research group, recruiting numerous new core, part-time, and adjunct faculty, and an experienced team of highly trained administrative and analytic staff. The current PETS