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

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