Intern - ML for Computational Fluid Dynamics

The internship focuses on applying machine learning and numerical optimization to enhance Computational Fluid Dynamics for aerodynamic design within a leading organization that develops unmanned aerial vehicles. You will join a top‑tier team that sits at the crossroads of aerodynamics and artificial intelligence, contributing to the evolution of UAV design. Your daily work will involve collaborating with both the aerodynamics and ML groups to create and refine algorithms that steer CFD investigations across the design space. You will test these concepts on practical cases, analyze the outcomes, and communicate the results to the team. The role also requires integrating the new methods into the existing design workflow, ensuring they are functional and reliable in production. Candidates must be able to commit to at least five months and possess a solid grasp of Gaussian processes and Bayesian optimization. Proficiency in Python development for shared codebases, including version control, peer reviews, and continuous integration, is essential. A strong understanding of mathematical optimization—such as convex and non‑linear programming—and a proactive attitude toward owning projects from idea through implementation are also required. Prior research experience, familiarity with CFD or large‑scale simulations, and exposure to additional machine‑learning techniques are considered advantageous.