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Field
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of materials mechanics, e.g., plasticity, porous plasticity, crystal plasticity and damage mechanics. Knowledge of micromechanical modelling. Knowledge of non-linear finite element methods. Knowledge of FFT
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in computational mechanics and a strong background in solid mechanics and the finite element method. Candidates should have a demonstrated experience in teaching undergraduate and/or graduate courses
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of building and structural concepts through the development of AI-enhanced Finite Element Method (FEM) tools. It includes implementing FEM-based systems capable of proposing innovative structural forms
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professional experience to include: Non-linear finite element modeling and simulation Design and testing of roadside safety devices Preferred Qualifications LS-DYNA, HyperMesh, Solidworks, computer programming
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background in computational methods, including finite element analysis and other advanced modelling techniques, would be advantageous. The successful candidate will be expected to deliver research-informed
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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
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-suited. By the end of the PhD, the candidate will have gained strong skills in experimental mechanics, test management, materials characterization, and numerical modeling, particularly finite element
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using both classical and finite element methods (FEM). Review detailed part or assembly definition prior to production release. Examine structural or material discrepancies and create associated
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innovative computational approaches, leveraging finite element simulations, AI, and clinical data, to better understand the mechanisms of MR. This aims to improve patient risk stratification and treatment
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analysis using finite elements of various mechanical assemblies and components when subjected to static, non-linear loads, dynamic, thermal, and/or seismic loads Regulatory destructive testing of Type A