
Internship / Master's Thesis: Physics-Informed ML for Vehicle Dynamics Identification (m/ f/ d)
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Gehalt
13,90 €/Std. ≈ 1.092 € netto im Monat bei 20 Std./Woche
Median für Tech in München: 17,26 €/Std., aus 100 Werkstudentenanzeigen mit Gehaltsangabe der letzten 12 Monate. In 98 % der übrigen 95 Anzeigen ist die Bezahlung höher. Gehaltsübersicht
Voraussetzungen
Studium: Informatik, Elektrotechnik oder Mechatronik · Masterstudierende · Immatrikulation erforderlich
Sprachen: Deutsch fließend · Englisch fließend
Arbeitszeit & Dauer
35 Std./Woche · 6 Monate
Gut zu wissen
Abschlussarbeit möglich
Arbeitgeber
CARIAD · Hauptsitz in Bochum
Wir listen 11 offene Werkstudentenjobs und Praktika bei CARIAD, davon 3 in München. Seit März 2026 haben wir dort 48 weitere gelistet, die inzwischen geschlossen sind. Alle Stellen von CARIAD
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Skills laut Anzeige
Symbolic RegressionPyTorchVehicle Dynamics ModelingPhysics-informed Machine LearningPythonSINDyMachine LearningSystem IdentificationControl Design
Stellenbeschreibung
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Beschreibung bereitgestellt von CARIAD
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.
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