Select how often (in days) to receive an alert: Postdoctoral Fellow, Composite Structure Virtual Testing Priority will be given to the following designated employment equity groups: women, Indigenous Peoples
* (First Nations, Inuit and Métis), persons with disabilities and racialized persons*.
* The Employment Equity Act, which is under review, uses the terminology Aboriginal peoples and visible minorities. Candidates are asked to self-declare when applying to this hiring process. OrganizationalUnit:Aerospace Classification:RO Duration: 2 years Work arrangements: Due to the nature of the work and operational requirements, this position may be eligible for a limited hybrid work arrangement (combination of working onsite and telework). The program Postdoctoral Fellow, Composite Structure Virtual Testing The role Certification-by-Analysis (CbA) of composite airframe structures has gained increasing interest as an alternative to full-scale tests to demonstrate compliance with regulatory requirements through modelling and simulation. Compared with high-fidelity finite element analysis (FEA), a computational framework based on Physics-Informed Mesh Graph Networks (PI-MGN) could provide fast, reliable, and data-efficient analysis to simulate the intricate failure mechanisms inherent to composite airframe structures. The goal of this project is to develop a PI-MGN solver by integrating deep learning with the governing physics of the problem, enabling models to achieve up to two orders of magnitude speedup over conventional FEA solvers, while maintaining the prediction accuracy required by CbA. Ultimately, this numerical tool will provide an efficient, trustworthy means to design, analyze, and optimize composite airframe structures, with quantified reliability. This will help shorten aerospace design cycles,



reduce development costs, and accelerate the deployment of innovative, sustainable aircraft technologies. Education PhD in mechanical, civil, or aerospace engineering with focus on computational mechanics of composite structures.
- Proven experience in composite structure damage modelling using the finite element method;
- Proven experience in Abaqus, including user-subroutine development and coding; and
- Proven experience and sound understanding of machine leaning theory and techniques applicable to engineering problems. Assets:
- Experience with modelling Verification, Validation, and Uncertainty Quantification (VVUQ);
- Hands-on experience in composite fabrication and mechanical testing;
- Experience in using finite element software such as Nastran/Patran, ANSYS, and/or StressCheck for structural analysis;
- Experience and understanding in structural optimization;
- Experience in data analysis, large dataset handling, and Python programming;
- Experience in the publication of research papers, demonstrated by authorship of publications in high-impact peer-reviewed scientific journals;
- Experience in delivering explicit presentations
- effectively translating complex concepts for diverse audiences to drive understanding and collaboration;
- Experience in working both independently and collaboratively in multi-disciplinary research teams;
- Experience in project management, including planning, organizing,



managing, monitoring, and completing multiple projects and initiatives. For a Reliability Status, verification of background information over a period of 5 years is required. Who is eligible?
- Fellowships will be for two years contingent on satisfactory progress achieved during the first year.
- Candidates should have obtained a PhD (or equivalent) within the past three years (PhD received on or after July 1, 2024*) or expect to complete their PhD within 6 months of appointment.
- Fellows will work in a lab under the direct supervision of an NRC researcher. *Exceptions may be considered to recognize personal or family related leave. Please connect with us using the contact information below to discuss in more detail. Compensation The intent of this hiring action is to staff the position at the AsRO level, which is an early-career level position with a salary range of $83,985 to $116,641. This wide range accounts for varying degrees of expertise and impact, and growth within the role. Salary determination will be based on a thorough evaluation of the successful candidate's prior experience. This evaluation will consider specific achievements, skills, and contributions that demonstrate the candidate's impact and expertise. As a guide, the average starting rate for new PhD graduates entering this role is $83,985.
- NRC employees also enjoy a wide-range of competitive benefits including a robust pension plan, comprehensive health and dental coverage, disability and life insurance, office closure at the end of December, and additional supports to enhance your well-being throughout your career and beyond. Discover the possible Anything is possible at the NRC, named in 2026 one of Canada's To

📌 Postdoctoral Fellow, Composite Structure Virtual Testing (Ottawa)
🏢 National Research Council of Canada
📍 Ottawa