PhD Intern, Physics-Informed Machine Learning

Autodesk • San Francisco, CA

2d • Onsite

About The Position

The Simulation, Optimization and Systems (SOS) Group at Autodesk Research is looking for a passionate and skilled research intern for Summer 2025 at our San Francisco office located in One Market, Ste. 400 . 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.

The 2026 U.S. program runs for 12 weeks (May 18 – August 7 or June 15 – September 4). All internships are paid. As an intern, you will contribute to meaningful projects, be mentored by industry leaders, and participate in tech talks and other activities designed to support your personal and professional development. Our internships align with Autodesk’s Flexible Workplace approach, which is designed to meet the needs of our business while providing flexibility in support of office, remote and hybrid work preferences.

Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made. We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world. When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!

Requirements

• Currently pursuing a PhD degree in Engineering, Physics, Mathematics, Computer Science, or other related disciplines
• 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

What This Job Offers

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