Publisher
source

Technical University of Munich

Top university

PhD Position in Machine Learning for Vehicle Aerodynamics at Technical University of Munich Technical University of Munich in Germany

Degree Level

PhD

Field of study

Computer Science

Funding

The position is in close collaboration with Audi AG and is based at the Technical University of Munich. Funding details such as stipend amount or tuition coverage are not specified in the announcement.

Deadline

Nov 1, 2026

Country flag

Country

Germany

University

Technical University of Munich

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Keywords

Computer Science
Machine Learning
Aerodynamics
Mechanical Engineering
Mathematics
Artificial Intelligence
Automotive Engineering
Statistics
Physics
Machinelearning
Neural Networks
Flow Simulations
Simulation Data

About this position

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.

Funding details

The position is in close collaboration with Audi AG and is based at the Technical University of Munich. Funding details such as stipend amount or tuition coverage are not specified in the announcement.

What's required

Applicants must hold a master's degree or equivalent in computer science, artificial intelligence, machine learning, mathematics, statistics, data science, physics, or a related field from a recognized university. Degrees from abroad must be equivalent to the corresponding German degree, subject to university approval. Candidates should have a strong interest in scientific research and experience with aerodynamics, fluid simulations, and neural networks. The final grade of the MSc program must be at least 2.0 (German system). Very good German and English language skills, strong analytical skills, high motivation, independence, structured work approach, and strong social and communication skills are required.

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Professors

Professor

University Name
Country flag

Germany

Research Interests

Automotive Engineering

Statistics

Neural Networks

Physics

Machine Learning