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science, or a closely related field. Experience with the finite element method. Proficient in C++ and Python. Skilled in Unix-based operating systems. Skilled in oral and written communications and presentations
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Eligibility criteria Numerical analysis and finite element method Solving anisotropic problems Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7340-SOPBAU-024/Default.aspx Work Location
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of the fabricated fibers within optical networks. - Design of innovative hollow-core fiber structures using the finite element method. - Fabrication of specific optical fibers on a fiber drawing tower. - Linear
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The successful candidate will hold a PhD in applied mathematics, and will have knowledge of PDE discretization methods such as finite elements, finite volumes, etc. Practical knowledge of at least one programming
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to completion of) a PhD/DPhil in geotechnical engineering, along with experience in numerical methods, including the implementation of soil constitutive models in finite element code. You should have excellent
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finite elements, finite volumes, etc. Practical knowledge of at least one programming language (preferably C++). English : level C2 _ All research exchanges will be in English Additional comments ERC
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candidate has experience in coding numerical solutions to partial differential equations via the finite element method / finite differences schemes and programming skills in Mathematica, Matlab, Python
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evaluation of alloys; familiarity with finite element methods. Skills and experience in programming, machine learning, or signal processing are all considered a plus. Outstanding UA benefits include health
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-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and uncertainty quantification. The position comes with a travel allowance and access
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and