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RAP opportunity at National Institute of Standards and Technology NIST Finite Element and Crystal Plasticity Modeling for the Development of Lightweighting Materials Location Material
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signals design and processing, and mutlitmodal sensing. The project welcomes expertise in robotics, serial communication protocols and microprocessors, signal processing, and finite element modeling, and
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properties to them, generates a finite element mesh, and performs virtual measurements. The goal is to build a computational platform that can predict the macroscopic behavior of a material from knowledge of
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materials. The primary focus of this work is on mechanical characterization, microstructural analysis, and finite element analysis (FEA) and artificial intelligence (AI)/machine learning (ML) modeling
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and utilize equipment such as high- and low- field NMR/MRI systems and non-traditional systems such as single-sided magnets. In addition to this equipment, techniques such as finite-element modeling and
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examples include using finite element and classical atomistic modeling to study nanoindentation, and using density functional theory and semiempirical tight binding to study the deformation, band structure
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-mechanical processing. Some of these modeling tools include density functional theory (DFT), CALPHAD-based models, phase-field models, and finite-element models (FEM) to predict as-built microsegregation
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complex permittivity and permeability characterization with on-wafer techniques, materials modeling (including finite element simulations, and theory), and the development of mm-wave and microwave
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thermomechanical finite element modeling, CALPHAD-based thermodynamics, and crystal plasticity and to both powder-scale and atomic-scale simulations. Emphasis will be on integration of model predictions with
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image correlation (DIC) datasets with fully three-dimensional finite element analysis (FEA) results in mechanical test setups on metals and polymers including uni-axial and bi-axial loading at different