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and hardware. Preferred Qualifications: Experience with solid modeling and finite element analysis codes as it relates to electromagnetic interactions of coil assemblies, the modeling of cryogenic
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for simulating such complex geometries. For example, the memory and computation time required become prohibitive with standard “black-box” finite element methods. The objective is therefore to develop a dedicated
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to isolate geometric features of interest for further dataset curation and training Develop tools which interface between the trained predictor / inference models and structural finite element model generators
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-assembly), to macroscopic finite element analysis (strength, stiffness, fracture toughness). Experimental techniques (SEM, XRD, Raman, TGA) will validate the models, enabling robust predictions of composite
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. Topics in parametric design and design optimization using Finite Element Analysis (FEA), Computer-Aided Design (CAD), and Manufacturing (CAM) are introduced in the classroom and online and then reinforced
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will have a strong background in numerical analysis and computational mathematics, with proven research contributions in finite element methods, boundary element methods, numerical methods for partial
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mathematics, with proven research contributions in finite element methods, boundary element methods, numerical methods for partial differential equations (PDEs) with uncertainties, and related areas
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be an asset: Robot Operating System (ROS). Finite Element Analysis simulation. Opto-electro-mechanical Systems. Power electronics and electrical machines. Familiarity with AWS and MS Azure platforms
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). Experience running engineering simulations and performing associated analysis of simulation results. This includes finite element methods or computational fluid dynamics (Simvascular). Confidence with
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include, for example, finite element analysis, computational fluid dynamics, or reduced-order system-level modeling. Demonstrated commitment to breadth of education, including educating the whole person in