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. Hu, X. Wei, X. Wu, J. Sun, J. Chen, Y. Huang, J. Chen, A deep learning-enhanced framework for multiphysics joint inversion, Geophysics, 88(1), K13-K26, 2023. https://doi.org/10.1190/geo2021-0589.1 [3
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Website for additional job details https://emploi.cnrs.fr/Offres/Doctorant/UMR5295-MATMAL-001/Default.aspx Work Location(s) Number of offers available1Company/InstituteInstitut de mécanique et
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Your Job: Digital methods for inverse materials design are essential to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so
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PhD project involves interdisciplinary research at the interface of computer science and mathematics and addresses a complex, coupled inverse problem with explicit uncertainty quantification. Research
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apply a data-driven approach to cross-scale materials discovery and design, in particular, goal-oriented, inverse design procedures based on process-structure-property linkages are of interest
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Austrian Academy of Sciences, the Johann Radon Institute for Computational and Applied Mathematics (RICAM) | Austria | 14 days ago
, Approximation Theory, Machine Learning, Inverse Problems and Regularization Theory. Proficiency in programming with a strong preference for Python and deep learning frameworks such as PyTorch is highly desirable
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: https://metatune.webs.upv.es/jobs/ (you can also find it at the end of the job description). Where to apply Website https://metatune.webs.upv.es/jobs/ Requirements Research FieldPhysicsEducation
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NIST only participates in the February and August reviews. There is a growing need for high-performance materials for various technological applications. To address this need, the NIST-JARVIS (https
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27 Feb 2026 Job Information Organisation/Company Ecole Nationale de l'Aviation Civile Research Field Engineering » Electronic engineering Researcher Profile Recognised Researcher (R2) Leading
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25 Mar 2026 Job Information Organisation/Company Université de Toulouse Research Field Computer science » Informatics Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First