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of computational fluid dynamics (CFD). Knowledge of finite element method (FEM). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction
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are developing AI that can map welding processes in the automotive industry based on simulation data. We are looking for a student assistant with an interest in machine learning and finite element simulation. Your
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skills. You’ll have strong skills in Finite Element Analysis (e.g. Abaqus, ANSYS), an understanding of machining processes, and a proactive, collaborative approach to problem-solving. The University
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to plasticity. (assessed at: application/interview) Experience in computational mechanics, especially numerical methods for solving field equations relevant to material mechanics, i.e., Finite Element schemes
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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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, engineering, materials science, maths, or computer science), or equivalent experience Experience with uncertainty quantification or error analysis Familiarity with numerical methods (e.g., Monte Carlo, Finite
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a variety of approaches, such as stochastic processes, kinetic theory, variational analysis, finite element methods, and data-driven techniques. The Vienna School of Mathematics doctoral program
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experience with HHG sources are a plus but not critical. Knowledge of programing languages such as Python (and with finite element simulations specifically) would be also valuable.
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time standing on hard and/or uneven surfaces. Lifting, climbing ladders, squatting, twisting, using sprayers, walking on a variety of surfaces, digging, planting and exposure to the elements
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a variety of approaches, such as stochastic processes, kinetic theory, variational analysis, finite element methods, and data-driven techniques. The Vienna School of Mathematics doctoral program