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- University of Twente (UT); 16 Oct ’25 published
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Field
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candidate will enjoy working on finite-element based modelling, the application of mathematical concepts from UQ/ML to practical problems, and an understanding of scripting/programming. Individuals with
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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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, computer science, or a closely related field. Coding experience for the computational modeling of physical and/or engineered systems, preferably with finite-element methods, is a must. Strong programming
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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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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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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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proficiency in three of the items in the following list: - Fluent programmer (e.g., python or other) - Fundamental of finite element analysis and experience with FEA software
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speeds far beyond those of traditional finite element (FE) methods. The key innovation lies in directly modelling discontinuity lines, such as cracks, rather than relying on iterative mesh refinements
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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