PhD Candidate for Systematic Integration of Real-World Data in Reinforcement Learning (f/m/d)

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PhD Candidate for Systematic Integration of Real-World Data in Reinforcement Learning (f/m/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!

The aim of our PhD Program is to promote innovative topics that are relevant to CARIAD. We cooperate with top universities and bring new research projects to life. Our PhD candidates get the opportunity to create new innovations in their projects for CARIAD and the respective scientific field. All PhD projects are accompanied by a supervisor professor and a dedicated CARIAD mentor. Essential trainings for the PhD candidates complete the PhD Program.

For the department Vehicle, Energy, Motion & Body (VEMB) we are looking for a PhD candidate for the project “Learning Intelligent Onboard Functions”. 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

• PhD project with the working title: Systematic Integration of Real-World Data in Reinforcement Learning
• Tackle key challenges in Reinforcement Learning with the focus on VEMB functions
• Review of the state-of-the-art in the subject area
• Leverage real world measurement data in the Reinforcement Learning training process
• Deploy and validate developed methods and controllers in real world experiments
• Collaborate with teams in pre-development and series development

WHO YOU ARE

• Master's degree in in a relevant field: Robotics, Electrical Engineering, Mechanical Engineering, etc.
• Expertise in control design and machine learning, especially in Reinforcement Learning
• Very good programming skills in Python and experience with machine learning frameworks such as PyTorch, TensorFlow, etc.
• Hands-on experience through real-world projects, such as student projects, internships, or prior work experience
• High level of commitment, initiative, and teamwork
• Fluent in English, German is a plus

NICE TO KNOW

• Duration: 3 years
• Working with high-ranked University
• Possibility to supervise students
• Remote work options
• Temporary work from abroad in selected countries
• 30 days paid leave
• Note:Please attach a transcript of records including a module overview to your application via the system after you have submitted your application. Otherwise the application cannot be processed
• Important: The PhD admission requirements are set by the university and candidates have to fulfill these requirements before starting their projects. Candidates need a confirmation by the supervisor professor before onboarding (not necessary for application)
• If you have further questions about the candidate journey at CARIAD, please contact us: careers@cariad.technology

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.

Job ID: 15119

Company: CARIAD SE

Location:

Mönsheim, DE, 71297

Working Model: Full-time

Contract Type: Fixed-term

Remote Working: By agreement

Why CARIAD?

We believe that how we work together is just as important as the technology we create. We strive to take action with a can-do attitude, and value speed over perfection. We aim to collaborate with mutual trust, taking accountability for our actions. We foster transparency and welcome diverse perspectives as we learn, adapt, and grow together

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