Composite Structures Machine Learning Fellow

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.

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