Intern - ML for Computational Fluid Dynamics

Are you interested in applying machine learning (ML) and numerical optimization to improve Computational Fluid Dynamics (CFD) for aerodynamic design? Always wondered how ML can be applied to engineering? If you can dedicate to us at least 5 months, then this is your chance to join a world-leading team at the forefront of UAV design and{{:}}

• Work at the intersection of the aerodynamics and ML teams to improve the design of our unmanned vehicles.
• Research and develop ML and numerical algorithms to guide the CFD exploration of aerodynamic design space.
• Evaluate your ideas on real-world test cases, assess the results, and present your findings and conclusions.
• Implement and deploy the algorithms in our design pipeline.

Requirements

Familiarity with Gaussian processes and Bayesian optimization.

• A desire to push the boundaries of ML for engineering and extend mathematical concepts with minimal supervision.
• Hands-on experience in developing Python code for shared repositories{{:}} git, code reviews, continuous integration.
• Knowledge of mathematical optimization concepts and algorithms, e.g., convex optimization, non-linear programming, etc.
• Mindset to take ownership of your work and follow it from concept to final implementation. (Experience in research is a plus.)
• Experience with CFD, large-scale simulations, and other ML is a plus.