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(or be near completion), with established expertise in Computational Mechanics, Constitutive Modelling, and the Finite Element Method. Informal enquiries may be addressed to Prof. Laurence Brassart
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
of the following areas: Large-deformation numerical modeling (e.g., Coupled Eulerian-Lagrangian (CEL), Material Point Method (MPM), or advanced Finite Element Methods). Physical modeling of tunnel
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. Candidates are expected to have a strong background in at least one of the following areas: numerical analysis and/or simulation methods for PDEs (in particular finite volume or finite element methods
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advanced modelling approaches—such as finite element analysis —to capture the nonlinear, multi-physics nature of soft materials. By integrating experimental data and validating simulations, your work will
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of computer codes. Specific Requirements Educational Requirements: Knowledge of mathematical and computational modeling with partial differential equations and the finite element method. Scientific programming
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expertise in the analysis and design of concrete structures. Advanced proficiency in Finite Element Modelling (FEM) using tools such as Abaqus, ANSYS, RFEM, SAP2000, MIDAS or equivalent. Solid understanding
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 4 hours ago
simulation (such as bonded particle) and Eulerian (such as finite element) methods can be used. Proposals should acknowledge the benefits and limits of their technique compared to others. Part of the proposal
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, computer science, or a closely related field. Coding experience for the computational modeling of physical and/or engineered systems, preferably with finite-element methods, is a must. Strong programming
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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