Internship / Thesis - System Identification with Physics-informed ML for Vehicle Dynamics (m/f/d)
Internship / Thesis - System Identification with Physics-informed ML for Vehicle Dynamics (m/f/d)
Berlin, DE, 10587 Mönsheim, DE, 71297 München, DE, 80807
Internship / Thesis - System Identification with Physics-informed ML for Vehicle Dynamics (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 of the current state of the art literature in physics-informed machine learning; evaluation of methods with regard to their suitability for modeling in vehicle dynamics.
- Evaluation and selection of suitable approaches to minimise the simulation-to-reality gap.
- Implementation, enhancement, and optimization of selected methods.
- Validation of developed models using synthetic and real-world vehicle data.
- Collaboration with teams in pre-development and series development.
- Work in cooperation 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-hour 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 careers@cariad.technology - 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.
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 of the current state of the art literature in physics-informed machine learning; evaluation of methods with regard to their suitability for modeling in vehicle dynamics.
- Evaluation and selection of suitable approaches to minimise the simulation-to-reality gap.
- Implementation, enhancement, and optimization of selected methods.
- Validation of developed models using synthetic and real-world vehicle data.
- Collaboration with teams in pre-development and series development.
- Work in cooperation 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-hour 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 careers@cariad.technology - we are happy to support you.
Berlin, DE, 10587 Mönsheim, DE, 71297 München, DE, 80807