Internship / Master's Thesis: Physics-Informed ML for Vehicle Dynamics Identification (m/f/d)

Location: München, DE, 80807

Internship / Master's Thesis: Physics-Informed ML for Vehicle Dynamics Identification (m/f/d)

Internship / Master's Thesis: Physics-Informed ML for Vehicle Dynamics Identification (m/f/d)

Job ID

31070

Company

CARIAD SE

Location

München, DE, 80807

Mönsheim, DE, 71297

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Department

Apprenticeship & Study

Career Level

Students

Working Model

Full-time

Contract Type

Fixed-term

Remote Working

According to company policy

Posting Date

Oct 20, 2026

Internship / Master's Thesis: Physics-Informed ML for Vehicle Dynamics Identification (m/f/d)

We are CARIAD, the automotive software company of the Volkswagen Group. Our teams build automotive software platforms and digital customer functions for iconic brands like Audi, Volkswagen, and Porsche – supporting the Volkswagen Group in becoming the leading automotive technology company. With CARIDIANS in Germany, the USA, China, Estonia, and India, we are transforming automotive mobility for everyone.

Join us and be part of this exciting journey!

YOUR TEAM

For the department Vehicle, Energy, Motion & Body (VEMB) we are looking for a student (intern or master thesis) for the project “System Identification with Physics-informed Machine Learning for Vehicle Dynamics”. Our department develops advanced software for vehicle energy, motion, and body systems. Our VEMB pre-development team works on methods for end-to-end learning of VEMB functions to enable faster, scalable and more cost-effective product development. We cover the entire development range-from initial concepts to proof of concepts in test vehicles in close cooperation with the series development departments.

WHAT YOU WILL DO

Review the state-of-the-art physics informed Machine Learning literature and evaluate methods for vehicle dynamics modeling (e.g., SINDy, Symbolic Regression, etc.)

Evaluate and select suitable approaches to minimize the Simulation to Reality gap

Implement, extend, and optimize the selected methods

Validate the developed models using both synthetic and real-world datasets

Collaborate with teams in predevelopment and series development environments

Work together with a PhD student in the field of Physics-informed Machine Learning and Simulation to Reality transfer

WHO YOU ARE

Enrolled student in a relevant field such as Computer Science, Robotics, Electrical Engineering, or Mechatronics, with a strong focus on machine learning

Knowledge of control design and machine learning (Physics-informed Machine Learning is a plus)

Experience with Python and machine learning frameworks such as PyTorch, etc.

Hands-on experience through real-world projects, such as student projects, internships, or prior work experience

Strong analytical and problem-solving skills

High level of commitment, initiative, and teamwork

Fluency in English and German and good communication skills

NICE TO KNOW

Remote work options within Germany

Duration: 6 months

35 hours/week

Salary: 13,90 €/hour

At CARIAD, we embrace individuality and diversity because we believe our differences make us stronger. We actively seek to build teams with a variety of backgrounds, perspectives, and experiences. Our goal is to create an environment where everyone feels valued and empowered to contribute. If you need assistance with your application due to a disability, please reach out to us at [email protected] - we are happy to support you.

We are CARIAD, the automotive software company of the Volkswagen Group. Our teams build automotive software platforms and digital customer functions for iconic brands like Audi, Volkswagen, and Porsche – supporting the Volkswagen Group in becoming the leading automotive technology company. With CARIDIANS in Germany, the USA, China, Estonia, and India, we are transforming automotive mobility for everyone.

For the department Vehicle, Energy, Motion & Body (VEMB) we are looking for a student (intern or master thesis) for the project “System Identification with Physics-informed Machine Learning for Vehicle Dynamics”. Our department develops advanced software for vehicle energy, motion, and body systems. Our VEMB pre-development team works on methods for end-to-end learning of VEMB functions to enable faster, scalable and more cost-effective product development. We cover the entire development range-from initial concepts to proof of concepts in test vehicles in close cooperation with the series development departments.

Review the state-of-the-art physics informed Machine Learning literature and evaluate methods for vehicle dynamics modeling (e.g., SINDy, Symbolic Regression, etc.)

Evaluate and select suitable approaches to minimize the Simulation to Reality gap

Work together with a PhD student in the field of Physics-informed Machine Learning and Simulation to Reality transfer

Enrolled student in a relevant field such as Computer Science, Robotics, Electrical Engineering, or Mechatronics, with a strong focus on machine learning

Knowledge of control design and machine learning (Physics-informed Machine Learning is a plus)

Hands-on experience through real-world projects, such as student projects, internships, or prior work experience

At CARIAD, we embrace individuality and diversity because we believe our differences make us st