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National Research Council Canada

Postdoctoral Fellow, Composite Structure Virtual Testing

Ottawa, ON, Canada

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.

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Job details

Pay: 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.
Apply by: Jan 8, 2027
Posting language: English
Posted: Oct 28, 2026

Original job description

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.

City: Ottawa

Organizational Unit: Aerospace

Classification: RO

Tenure: Term

Duration: 2 years

Language Requirements: English

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

The NRC’s Postdoctoral Fellowship program offers PDFs access to unique world-class facilities and the opportunity to work alongside multi-disciplinary teams of expert researchers and technicians on projects of critical importance to Canada. PDFs will carry out research on innovative research projects, with opportunities for career development (publications and/or industry interaction).

PDFs will be offered appointments to the staff of the National Research Council on a term basis and will be offered salaries and benefits currently available to Research Officers. Salaries commensurate with experience are taxable and subject to other deductions. As a guide, the current annual PhD recruiting rate is $83,985. In addition PDFs will be allocated a travel fund to support their work.

Project title

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.

For information on certificates and diplomas issued abroad, please see Degree equivalency

Experience

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 clear 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.

Language requirements

English

Information on language requirements and self-assessment tests

Condition of employment

Reliability Status

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.

Application requirements

To be considered for the program please include the following in your application, please note that you will need to attach the required documents as per the list below when submitting your application. Failure to do so will result in your application being excluded.

Resume
Statement of Interest in the project (maximum one page in length)
PhD Transcript - an electronic copy is sufficient, it does not have to be an official version.
List of Publications

When submitting your application you can include the required documents in any attachment field such as «Second language evaluation results» or «Other attachments»

In addition, applicants who best meet the requirements of the position will be asked to provide three letters of recommendation at a later stage of the competition process.

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.

The NRC is committed to supporting career progression within the organization, providing access to the opportunities and resources needed for a pathway to a fulfilling career. 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 Top Employers for Young People, Top Employer in the National Capital Region and Forbes Canada’s Best Employers!

As Canada’s largest research and innovation organization, our world-renowned research pushes the boundaries of science and engineering to make the impossible, possible. Every day we explore new ideas through innovative research and help companies discover possibilities that impact Canada’s future and the world.

At the NRC, you’ll also discover new possibilities. Our supportive workplace fosters a culture of creativity, welcoming fresh perspectives and innovation at all levels. We value teamwork. You’ll collaborate across multiple fields and with the brightest minds to find creative solutions. Most importantly, you’ll discover what’s possible within you as you grow, make valuable contributions and progress in your professional journey. From ground-breaking discoveries to a life-changing career, discover your possible at the NRC.

Notes

In 2026, the NRC was chosen as one of Canada’s Top Employers for Young People, a National Capital Region Top Employer and Forbes Canada’s Best Employer.
Relocation assistance will be determined in accordance with the NRC's directives.
Fellowships are open to nationals of all countries, although preference will be given to Canadian citizens and permanent residents of Canada. Please include citizenship information in your application.
The incumbent must adhere to safe workplace practices at all times.
We thank all those who apply, however only those selected for further consideration will be contacted.

Please direct your questions, with the requisition number (26215) to:

E-mail: NRC.NRCHiring-EmbaucheCNRC.CNRC@nrc-cnrc.gc.ca

Telephone: 4506454208

Closing Date: 7 January 2027 - 23:59 Eastern Time

For more information on career tools and other resources, check out Career tools and resources

*If you are currently a term or continuing employee at NRC, please apply through the SuccessFactors Careers module from your NRC computer.

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More about this job

Responsibilities

The postdoctoral fellow will develop a Physics-Informed Mesh Graph Network (PI-MGN) solver to simulate failure mechanisms in composite airframe structures. This role aims to provide a fast and reliable computational framework to support Certification-by-Analysis (CbA) and optimize aerospace design cycles.

Requirements

Candidates must hold a PhD in mechanical, civil, or aerospace engineering with a focus on computational mechanics of composite structures. Proven experience in finite element analysis, specifically Abaqus and machine learning theory, is required.

Skills

Composite structure damage modelling
Finite element method
Abaqus
Machine learning
Physics-informed mesh graph networks
Computational mechanics
Python programming
Data analysis
Structural optimization
Verification, validation, and uncertainty quantification
Mechanical testing
Composite fabrication
Project management
Technical writing
Public speaking

Benefits

Pension plan
Health coverage
Dental coverage
Disability insurance
Life insurance
Travel fund
Office closure at the end of December

Visa sponsorship

Not detected in the job text

Keywords

Composite structures
Certification-by-analysis
Finite element analysis
Physics-informed machine learning
Mesh graph networks
Aerospace engineering
Computational mechanics
Abaqus
Nastran
Patran
Ansys
StressCheck
Python
VVUQ
Structural optimization
Data science
Research and development
Composite fabrication
Mechanical testing
Project management
Scientific publishing
Deep learning
Numerical simulation
Uncertainty quantification
Reliability status

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