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Field
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• Excellent command of English, both written and spoken. Desirable qualifications • Experience with battery development, electrochemical testing, and reliability assessment • Familiarity with finite element
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loading conditions. By generating datasets from finite element simulations, ML models can learn the mapping between unit cell design parameters and homogenised properties. State-of-the-art approaches
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potencia y frecuencias./Finite element study of undesired electromagnetic effects due to high power and frequencies. Where to apply E-mail s.carrazoni@upm.es Requirements Research FieldEngineering
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heavy software development component. The successful candidate will perform research in the application of machine learning (ML) techniques to the finite element method (FEM) in the context of composites
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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from the fetal MRI scans. Additionally, mesh modelling techniques will be developed to create 3D Finite Element (FE) mesh models from the segmented fetal and maternal anatomy. These FE models will then
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learning tools for the prediction of composite manufacturing processes. You will work on development of algorithms, custom written codes, application of commercial finite element software and development
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of building performant, maintainable software. Hands-on experience with physically-based simulation, particularly cloth, deformable solids, or mass-spring/finite-element methods. Strong understanding
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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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finite element modeling programs such as SCIRun, COMSOL, or similar Special Instructions to Applicants: Applications should include a CV/resume, a brief (1 page maximum) statement of their career goals