Intern - ML for Computational Fluid Dynamics at Destinus

The role is a research position focused on applying machine learning and numerical optimization to improve computational fluid dynamics for unmanned aerial vehicle aerodynamic design. Responsibilities include developing ML and numerical algorithms, evaluating them on real-world test cases, presenting findings, and implementing and deploying the algorithms in the design pipeline. The posting requires at least five months of commitment and lists familiarity with Gaussian processes and Bayesian optimization, Python development, mathematical optimization, and research experience as preferred qualifications.