TIMEZYX is a deep-tech company, focused on advancing the frontiers of Structural Digital Twin and revolutionizing risk assessment for critical infrastructure. Our mission is to solve the most complex computational challenges in science and engineering using innovative mathematical models, high-performance computing, and cutting-edge machine learning techniques. At the core of our work is the development of robust numerical solvers and next-generation simulation tools with applications in solid mechanics, physics-based modeling, and uncertainty quantification.

We are proud to foster an inclusive, multidisciplinary research environment that brings together experts from structural engineering, computational mathematics, physics, computer science, and material science. As we continue to expand, we are looking for brilliant minds to join our team and help shape the future of risk assessment for critical infrastructure.

You can learn more about us on our website.

Position Overview: TIMEZYX is seeking a

Research Associate in Computational Structural Mechanics & Dynamics

to join our Engineering team in Vancouver, BC, Canada. This full-time, in-person role focused on developing new computational methods and conducting industrial projects. This is a unique opportunity to work on challenging and high-impact projects at the intersection of numerical analysis, scientific computing, and machine learning.

The successful candidate is an independent thinker with deep expertise in computational methods for solid mechanics and structural dynamics, and a passion for developing robust, efficient, and high-performance computational software.

Key Responsibilities:

Formulate, implement, and optimized 3D non-linear finite element models for structural systems with a special emphasis on concrete and steel structures.

Perform,



validate and benchmark advanced static and dynamic finite element analyses of complex structural problems including full-scale structures such as bridges, using open-source and proprietary simulation software.

Develop and apply computational methods for structural dynamics, including modal, frequency-domain, and transient dynamic analyses

Integrate solid mechanics and structural dynamics formulations into ML frameworks in collaboration with the PIML/PINN team

Apply expertise in concrete and steel structures to guide and validate computational models for real-world structural applications

Engage in technical discussions and present research findings in team meetings, technical workshops, and conferences

Maintain high-quality, well-documented computational codebases and contribute to collaborative research and industrial projects

Qualifications:

Ph.D. in Computational Mechanics, Computational Physics in Material Science, Structural Dynamics, Structural Mechanics, Structural Engineering, Mechanical Engineering, or a related field, with a strong focus on computational methods

Deep expertise in computational solid mechanics and structural dynamics, with strong theoretical foundations and practical experience in numerical modeling and simulation

Advanced knowledge of 3D nonlinear FEM, including solid elements, material/geometric nonlinearities, and nonlinear solution methods

Strong knowledge of structural dynamics, including modal,



frequency-domain, and transient analyses

Experience in full-scale nonlinear and dynamic structural analysis

Advanced knowledge of concrete structures and steel structures

Exposure to programming environments used in scientific computing (e.g., Python, MATLAB) for pre/post-processing of FE results

Excellent technical communication skills

Non-Technical Qualifications:

Robust communication and presentation skills

Ability to lead technical discussions and collaborate across disciplines

Self-motivated and capable of managing research projects independently

Strong team leadership, problem-solving, and decision-making abilities

Adaptable to evolving tasks and research directions

Nice to Haves:

Experience working in academic as well as industry collaborations

Proficiency with DIANA FE software and experience with ABAQUS is a strong asset

Familiarity with Physics-Informed Neural Networks (PINNs) or physics-based machine learning methods

Compensation:

This is a full-time, in-person position based in Vancouver, BC.

The salary range for this position is CA$90 - CA$110k

Why Join TIMEZYX?

Cutting-Edge Research:

Work on state-of-the-art numerical and machine learning methods to solve real-world scientific problems

Collaborative Environment:

Join a team of passionate, interdisciplinary researchers working across applied math, physics, and computing

Professional Development:

Opportunity to publish, present, and grow your research and industrial portfolio

Inclusive Culture:

A multicultural workplace committed to equity, diversity, and inclusion

Does this opportunity excite you? Join TIMEZYX in building the future of infrastructure intelligence

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📌 Computational Structural Mechanics & Dynamics (Winnipeg)
🏢 TIMEZYX
📍 Winnipeg