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Internship at Sauber Alfa Romeo Motorsports supported by SISSA mathLab research Team

Improvement of RANS turbulence modelling capabilities through Machine Learning

Sauber Motorsport AG (Hinwil, ZH, Switzerland)

Requirements: students from Mathematical, Mechanical or Aerospace Engineering courses. A solid knowledge in fluid dynamics and numerical analysis, as well a passion for programming (Python and C++, preferably), are a must. Some knowledge of Machine Learning foundations is a strong asset.

Description: The study of Formula 1 car aerodynamic performances involves the use of computational methods to approximate the Navier-Stokes equations at high Reynolds numbers, among other tools. These methods can use a variety of different modelling techniques to deal with the turbulent nature of the flow, where an increased accuracy typically corresponds to a higher computational cost.

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