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-time data acquisition during production, consulting, and prototype manufacturing. Graph neural networks provide an opportunity to operate on Mesh structured data utilized in Finite Element Method (FEM
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moving through different fluids. In this project, we are interested in developing moving mesh finite element methods for their dynamical simulation. We aim to produce efficient, accurate and robust
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assigned. Required Qualifications PhD in engineering, physics, mathematics or other related applied sciences. Five years of experience in theoretical and numerical evaluation of finite element methods
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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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and carry out finite element method (FEM) simulations. Our developments focus on higher efficiencies, more cost-effective manufacturing processes and materials, improved long-term stability and new
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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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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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current, relevant industry experience. Proficient communication and computer skills are a must. The ideal candidate will possess some or all of the following skill sets: Basic Finite Element Analysis
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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.