Research Intern, Physics-Informed Machine Learning

Autodesk • Toronto, ON

13d • Hybrid

About The Position

The Simulation, Optimization and Systems (SOS) Group at Autodesk Research is looking for a passionate and skilled research intern for Fall 2026 at our Toronto office. The ideal intern will conduct research and develop prototype code to combine machine learning with computational tools for analyzing air flow and heat transfer in building environments.

Requirements

• Currently pursuing a PhD degree in Engineering, Physics, Mathematics, Computer Science, or other related disciplines with a graduation date no earlier than January 2027.
• Experience with data-driven methods in simulation.
• Experience with development of physics simulation tools and numerical solvers.
• Experience with AI model training and ecosystems (PyTorch, TensorFlow, Flax etc).
• Experience and excellent knowledge of Python.
• Experience in publishing at top-tier conferences and journals.

Responsibilities

• Research on energy analysis tools for natural ventilation and their parameterization using CFD simulations.
• Conduct original research in developing or applying novel techniques in physics-informed machine learning.
• Implement prototypes to test and demonstrate the ideas and methods.
• Work with both open-source libraries and in-house libraries to develop the prototypes.
• Write documentation of the work, either as academic publication or internal white paper.
• Contribute to the technical expertise of the SOS Group by conducting learning sessions.

Benefits

• All internships are paid.
• Mentored by industry leaders.
• Participate in tech talks and other activities designed to support your personal and professional development.

What This Job Offers

• Career Level: Intern
• Education Level: Ph.D. or professional degree