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hands-on attitude is an absolute must, and hands-on familiarity with CAD and finite element design is needed. Accelerator or Large-Scale R&D Experience: A strong understanding of accelerator systems
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metal-organic frameworks, 2D/hierarchical structures, nanoparticles, single-atom catalysts), analytical instrumentation (including electronics and LabVIEW FPGA programming), finite element modelling using
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(variational multiscale, multiscale finite elements, etc.), structure preserving numerical methods, stochastic optimization, analysis of machine learning methodologies, multilevel methods, scale-bridging and
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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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techniques and associated tools (examples include, but are not limited to machine learning, density-functional-theory, materials informatics, finite-element modelling, phase-field modelling), and demonstrated
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of tissue response at spatiotemporal scales not achieved before, with complimentary techniques. We will establish finite element (FE) models of the human cochlea in (not limited to ABAQUS, ANSYS, etc.), using
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would be given additional credit in evaluation Experience in using Finite Element Codes such as ABAQUS, ANSYS, etc. for numerical analysis Ability to conduct high quality research and publish results in
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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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professional staff across the University, will provide meaningful support for your teaching and scholarship as elements of your personal and career development. We encourage you to grow professionally, and we