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-reviewed journals and presenting at professional conferences. • Demonstrate proficiency in experimental methods, and numerical/computer modeling and analysis, including areas such as finite element
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emerging fields. Courses 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
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for: The development of multi-scale models of polyelectrolyte gels using asymptotic methods. Numerically solving the models using, for example, the finite element method. Collaborating with experimental researchers
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the muscle architecture of the larva within the generative process. We recently extracted the muscles of the Drosophila larva body from a CT-scan recording. Furthermore, we developed a finite element
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modeling and simulation (e.g., finite element analysis, discrete event simulation). Experience with Infrastructure as Code tools (e.g., Terraform, Ansible). Experience with HPC clusters and workload
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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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fundamentals Technical writing Data analysis MATLAB, Excel, PowerPoint, CAD Preferred skills/experience areas include: Finite element analysis Experimental test conduct Acoustics Dimensional tolerance stack-up
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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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mechanical drawings for use and knowledge documentation. • Basic understanding of ABAQUS, Ansys, or other similar finite element programs. • Demonstrated ability to be self-taught and perform topic research