The Technical University of Munich (TUM) is offering a PhD position in the research field of machine learning for vehicle aerodynamics, in close cooperation with Audi AG. This opportunity is situated within Prof. Bjoern Thuerey's research group, a leading AI research laboratory in Germany and Europe, focusing on scientific machine learning (SML) and its application to aerodynamic simulations for vehicle development. The project aims to advance the state-of-the-art in data-driven aerodynamic simulation methods by combining modern machine learning techniques with the interpretability and accuracy of conventional simulations. The research will involve developing neural networks and AI surrogates for partial differential equations (PDEs), with the goal of improving simulation runtime and accuracy for automotive applications. The group is at the forefront of integrating AI with classic numerical solvers, as evidenced by their recent publications and open-source contributions in areas such as neural operators, hybrid PDE solvers, and diffusion models for fluid dynamics. Applicants should have a strong background in computer science, artificial intelligence, machine learning, mathematics, statistics, data science, physics, or a related field. Experience with aerodynamics, fluid simulations, and neural networks is highly desirable. Candidates must hold a master's degree or equivalent, with a final grade of at least 2.0 (German system), and possess very good German and English language skills. Analytical skills, motivation, independence, and strong communication abilities are essential. The successful candidate will work at the interface between science and industrial application, collaborating closely with Audi AG and leading researchers in scientific machine learning. The position offers the chance to contribute to the next generation of data-driven simulation methods and to be part of a vibrant research community at TUM. Funding details are not specified, but the position is embedded in a high-profile, industry-collaborative environment. For more information, visit the research group page at https://ge.in.tum.de/ and review the official job posting. Interested candidates should prepare their application documents and follow the application instructions provided by TUM.