INTERNSHIP DETAILS

Internship / Masters Thesis - Sim‑to‑Real Transfer in Reinforcement Learning (f/m/d) Internship / Masters Thesis - Sim‑to‑Real Transfer in Reinforcement Learning (f/m/d) Internship / Masters Thesis - Sim‑to‑Real Transfer in Reinforcement Learning (f/m/d)

CompanyVolkswagen AG
LocationMönsheim
Work ModeOn Site
PostedFebruary 17, 2026
Internship Information
Core Responsibilities
The role involves working with a PhD student on reinforcement learning and sim-to-real transfer, reviewing state-of-the-art methods like domain randomization and policy transfer. Responsibilities include investigating advanced randomization techniques and using real-world data to reduce the simulation-to-reality gap.
Internship Type
full time
Company Size
106947
Visa Sponsorship
No
Language
English
Working Hours
35 hours
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About The Company
The Volkswagen Group with its headquarters in Wolfsburg is one of the world’s leading automobile manufacturers and the largest carmaker in Europe. The Group is made up of ten brands from seven European countries: Volkswagen, Volkswagen Nutzfahrzeuge, ŠKODA, SEAT, CUPRA, Audi, Lamborghini, Bentley, Porsche and Ducati. Our group sells vehicles in 153 countries and operates 114 production plants worldwide. Each working day, around 675,000 employees worldwide produce cars, are involved in vehicle-related services or work in the other fields of business. Our goal is to make mobility sustainable for us and for future generations. Our promise: With electric drive, digital networking and autonomous driving, we make the automobile clean, quiet, intelligent and safe. At the same time, our core product becomes even more emotional and offers a completely new driving experience. It is also becoming part of the solution when it comes to climate and environmental protection. In this way, the car can continue to be a cornerstone of contemporary, individual and affordable mobility in the future. #Shapingmobility Imprint & Legal: http://vw.de/legal-notice DAT: http://vw.de/dat
About the Role

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) to work on a research topic in the area of reinforcement learning and simulation‑to‑real transfer. Our department develops advanced software solutions for vehicle energy, motion, and body systems. Within VEMB, our pre‑development team focuses on learning‑based methods for control and decision‑making, aiming to enable faster, scalable, and more cost‑effective development of onboard functions. A central challenge in this context is the reliable transfer of reinforcement learning policies trained in simulation to real systems, which requires systematic approaches to handle model uncertainties and real‑world variability. 
 

WHAT YOU WILL DO

  • Work together with a PhD student in the field of reinforcement learning and sim‑to‑real transfer
  • Review the state of the art in domain randomization, adaptive reinforcement learning, and policy transfer
  • Investigate advanced domain randomization techniques to improve robustness and real‑world performance of simulation‑trained reinforcement learning policies
  • Use real‑world measurement data to reduce the simulation‑to‑reality gap by tuning, adapting, or constraining simulation models
  • Design and conduct experiments to systematically evaluate the impact of different randomization and adaptation strategies
  • Assist in implementing prototype learning pipelines and validate developed methods in simulation and selected real‑world experiments
  • Collaborate with teams in pre‑development and series development environments 

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
  • Strong foundation in machine learning and reinforcement learning, including a solid understanding of modern learning algorithms and training paradigms
  • Solid programming skills in Python and hands‑on experience with modern ML frameworks (preferably JAX)
  • Experience with designing, training, and evaluating learning‑based models in simulation environments
  • Basic understanding of control systems, simulation, or physical modeling is a plus
  • Structured and independent working style with strong analytical and problem‑solving skills
  • 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.

Key Skills
Reinforcement LearningSim-to-Real TransferDomain RandomizationAdaptive Reinforcement LearningPolicy TransferPythonJAXMachine LearningSimulation EnvironmentsControl SystemsPhysical ModelingAnalytical SkillsProblem-Solving Skills
Categories
EngineeringScience & ResearchSoftwareData & AnalyticsTechnology