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Field
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structural health monitoring and simulation. Good written and oral communication skills Proficiency in ABAQUS, and lab test skill Ability to work independently and develop solutions under strict timelines We
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models and their implementation (such as in FEniCS/FEniCSx and in Abaqus via user-element and user-material subroutines (UEL/UMAT)), preparing manuscripts for peer-reviewed journals and presenting results
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to work independently to develop Abaqus of metallic kirigami and develop reduced-order analytical models to understand and describe observed behaviour. They will also be expected to contribute to writing
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Python programming, gaining valuable research experience alongside your studies. You will collaborate with an experienced, interdisciplinary research team and contribute to the development of Abaqus
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civil engineering or related field. At least 1 year of relevant experience in machine learning and structural health monitoring. Good written and oral communication skills. Proficiency in ABAQUS, and lab
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and at most 17 hours. Be part of change CAE modelling and numerical simulation (e.g. sheet metal forming processes using ABAQUS, etc) Develop data-driven and machine learning-based models to improve
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communication skills Proficiency in ABAQUS, and lab test skill Ability to work independently and develop solutions under strict timelines We regret to inform that only shortlisted candidates will be notified. Hiring
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | 6 days ago
candidate should have experience in finite element analysis software packages, such as Abaqus. The candidate should have experience in scientific computing with Python, including familiarity with
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Abaqus/Standard - linear and nonlinear strength, stability, contact interaction, and related analyses; SIMULIA Abaqus/Explicit - analysis of impact, landing, and rapidly varying loads; SIMULIA fe-safe
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communication skills Proficiency in ABAQUS, and lab test skill Ability to work independently and develop solutions under strict timelines We regret that only shortlisted candidates will be notified. Hiring Institution