Advance composite structures research with machine learning and finite element analysis in a limited hybrid Aerospace work arrangement.
The National Research Council's two-year Postdoctoral Fellow project develops a Physics-Informed Mesh Graph Networks solver. The work integrates deep learning with governing physics for composite airframe Certification-by-Analysis. You will study damage mechanisms, prediction accuracy, and quantified reliability through computational mechanics.
Key Responsibilities:
- Develop a Physics-Informed Mesh Graph Networks solver
- Integrate deep learning with governing composite-structure physics
- Model composite airframe damage mechanisms using computational methods
- Support efficient, reliable Certification-by-Analysis of structures
- Work under direct supervision in an NRC research laboratory
Requirements:
- PhD in mechanical, civil, or aerospace engineering
- Focus on computational mechanics of composite structures
- Experience modelling composite damage with finite elements
- Experience with Abaqus user-subroutine development and coding
- Understanding of machine learning for engineering problems
Apply machine learning, Abaqus, and computational mechanics to composite airframe analysis as an NRC postdoctoral researcher.
Composite Structures Machine Learning Fellow employer: National Research Council of Canada
At NRC, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. Our hybrid Data Scientist role offers not only competitive benefits but also ample opportunities for professional growth through engagement with Canadian SMEs and cutting-edge analytics projects. Join us in a dynamic environment where your contributions directly impact client success and drive technological advancements.
Contact Details:
National Research Council of Canada Recruitment Team
