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. Particular emphasis will be placed on characterizing defect populations and incorporating this information into finite element models, either through direct reconstruction of microstructures or statistically
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placed on characterizing defect populations and incorporating this information into finite element models, either through direct reconstruction of microstructures or statistically generated synthetic
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that the master's degree has been awarded. Programming skills, e.g., Fortran or C++, is a requirement. Competence in the theory of the finite element method is an advantage. Competence in the theory
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to the development of next-generation AI-enhanced finite element methods for robust structural design. You will be part of a dynamic and internationally oriented research group with strong expertise in solid mechanics
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of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Knowledge of constitutive modelling of materials. Knowledge of non-linear finite element methods. Knowledge
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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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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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is also placed on your: background in structural and fluid dynamics of civil and marine structures experience in finite element analysis and ability to code in Python or similar software motivation and
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dynamics of civil and marine structures experience in finite element analysis and ability to code in Python or similar software motivation and potential for research within the field professional and
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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