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to improve the accuracy of discontinuous Galerkin (DG) finite element methods. Recent work by our collaborator at the University of Tennessee has shown that optimal placement of high-order nodes can
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to improve the accuracy of discontinuous Galerkin (DG) finite element methods. Recent work by our collaborator at the University of Tennessee has shown that optimal placement of high-order nodes can
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innovative computational approaches, leveraging finite element simulations, AI, and clinical data, to better understand the mechanisms of MR. This aims to improve patient risk stratification and treatment
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of building and structural concepts through the development of AI-enhanced Finite Element Method (FEM) tools. It includes implementing FEM-based systems capable of proposing innovative structural forms
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for 3D stress distributions. Knowledge of computer software for finite element simulations Specific Requirements • PhD degree in mechanical engineering related to the specialization in metal science
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Finite Element Analysis. The candidate will work with a team of experienced researchers within the fields of concrete technology, structural design and computational mechanics. You will report to
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signal analysis Computational acoustics methods such as finite element, boundary element analysis or other computational methods development Flow excitation mechanisms such as gust response and turbulent
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will leverage expertise with computer-aided design, engineering and prototyping, Finite Element Analysis (FEA), and applications of AI and machine learning. Encouraged to develop and teach laboratory
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roles may be sufficient) Familiarity with computational tools for NDT simulation and data analysis, including finite element modeling (FEM) and digital image correlation (DIC) A commitment to pedagogical
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to Earthquake Engineering. Strong skills in nonlinear mechanics, soil-structure interaction, and beam finite element theory are particularly desirable. Proficiency in programming, especially using Matlab