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of the position. The successful candidate will have a solid theoretical foundation in one or more of the topics: Computational Mechanics, Finite Element Analysis (FEA), Numerical Optimization
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on physics-based computational modelling. Key activities include crystal-plasticity-based finite-element (CPFE) simulations, unit-cell and microstructure-resolved models, and the development of modelling
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, solid mechanics, and mechanics of materials Experience from numerical simulations, preferably finite element analysis Excellent written presentation skills and oral English language skill PLEASE NOTE: For
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theoretical foundation in one or more of the topics: Computational Mechanics, Finite Element Analysis (FEA), Numerical Optimization, or Materials Science. More specifically, we are looking for candidates with
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element methods. Knowledge of aluminium alloys Experience using non-linear finite element software, e.g., Abaqus. Experience with programming using Python and Fortran. Experience with conducting
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in English Solid knowledge in finite element analysis (FEA) and strong skills in FEA software such as ABAQUS Hands-on experience in the construction and application of deep learning neural networks
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suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme Excellent oral and written presentation skills in English Solid knowledge in finite element
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materials is deemed advantageous. Experience with numerical simulations (e.g., Finite Element, Finite volume, and other techniques) and programming (e.g., Python and MATLAB) is deemed advantageous. Experience