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developed using finite element analysis (FEA) between LMGC, ICube and LEM3 Labs to model the behaviour of Wharton's jelly samples in an ex vivo and in vivo context. Predictive tools, based on previous models
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structure-preserving discretization algorithms (a refinement of finite-element analysis compatible with exact geometric, topological, and physical constraints) with artificial neural networks for achieving
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heavy software development component. The successful candidate will perform research in the application of machine learning (ML) techniques to the finite element method (FEM) in the context of composites
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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material property database for composites. The candidate will utilize the database to develop AI models for composite discovery. The candidate will work with a multidisciplinary team to set up finite element
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computational fluid dynamics (CFD), cardiovascular modeling, or biomechanical growth and remodeling. Demonstrated experience with numerical methods (e.g., finite element method), programming languages (C
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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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computational models (e.g., Convection-Diffusion Equations, Finite Element Methods, Computational Fluid Dynamics) to investigate cerebral blood flow (CBF), neurovascular coupling (NVC), and neurovascular system
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. Ability to perform finite element simulations using software such as COMSOL, ANSYS, or ABAQUS. Experience in utilizing these tools for in-depth analysis is highly desirable. Required License/Registration
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transformations and also of finite element simulations of shape memory alloys. Physical experiments involve differential scanning calorimetry, thermomechanical testing, and potential nanoindentation of shape memory