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Field
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of relevant work experience, or equivalent combination of education and work experience. Preferred Qualifications Strong background in Finite Element Analysis. FLSA Exempt Full Time/Part Time Full Time Number
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, natural language processing (NLP) and machine learning Connected and autonomous vehicles Disaster and natural Hazard assessment and management Discrete element method (DEM) and finite element methods (FEM
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coding. Experience or exposure to finite element analysis (FEA) for structural analysis of mechanical components using SolidWorks Simulation or equivalent software. Experience or exposure to electronics
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in one or more of: Computer vision and Machine learning in development of AI for anatomical landmark detection and/or finite element modelling 2D/3D image segmentation using deep learning 3D–2D
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design, timber construction, CNC fabrication, robotic assembly, plate structures, semantic data models, finite element analysis, and plugin development. The PhD position (full-time) will span 4 years
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of academic background and scientific publications (45%); Experience in analysis and simulation and finite element software for the analysis of building components; energy and thermal simulation (30
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characterization, and/or component validation and verification. Strong background in CAD, finite element analysis, additive manufacturing, integrated computational materials engineering techniques and software
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management. Demonstrated experience in one or more applied computational fields: application of modern machine learning methodology, algorithms, computational modeling, finite element analysis, computational
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Finite Element Analysis (FEA) tools is highly valued. Knowledge of welding processes is a plus. Fundamental knowledge of Geometric Dimensioning and Tolerancing (GD&T ASME Y14.5). Enthusiasm for working in
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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