At CARIAD, we're bundling and further expanding the Volkswagen Group's software expertise. We’re uniting over 6,500 global experts to build a scalable technology stack, including a software platform, unified electronic architecture and reliable connection to the automotive cloud. Our CARIDIANS are developing vehicle functions such as driver assistance systems, a next-generation infotainment platform, power electronics and charging technology, and digital services in and around the vehicle.
Our software can already be found in Volkswagen ID. models and will soon power Audi and Porsche vehicles with the E3 1.2 platform in 2024.

It's no easy task, but with experts like you, we can shape the future of mobility. Join us at CARIAD and be part of this exciting journey!

YOUR TEAM

Our Charging & Energy team is looking for a Master's thesis student to collaborate with a PhD student on developing a real-time learning algorithm for an intelligent charging agent for Bidirectional Electric Vehicles (BEVs). The main research question is: What are the cutting-edge optimization algorithms that continuously learn from users' mobility data to generate charging schedules?
As part of our team, you’ll be involved in pioneering machine learning research, helping to advance charging functions on a high-performance computing platform. Our applications focus on cost-effective and CO2-neutral optimized charging to enhance user's experience in e-mobility.
This Master's thesis offers a hands-on opportunity to work on innovative smart charging systems, including the novel bidirectional electric vehicle concept. Join us and gain invaluable experience in the field of sustainable e-mobility!

WHAT YOU WILL DO

• Conduct literature review on state-of-the-art real-time learning techniques for RL agents
• Design and implement selected solutions on CARIAD's development platform
• Analyse and evaluate smart charging algorithms using real-world data
• Document and present findings for your final Master thesis
• Prepare and present the results in a scientific paper

WHO YOU ARE

• Enrolled student in Computer Science, Electrical Engineering, Electronics, Telecommunications, or a related engineering field
• Strong programming skills in Python
• Familiarity with optimization algorithms, machine learning, or reinforcement learning (through coursework or prior knowledge)
• Hands-on experience in data processing and algorithm development
• Team-oriented, communicative, and proactive in problem-solving
• Experience with smart energy systems and electric mobility is a plus

NICE TO KNOW

• Remote work options
• Thesis duration: 6 months
• 35-hour weeks
• If you have further questions about the candidate journey at CARIAD, please contact us:

YOUR RECRUITING CONTACT

Sandra Lehenmeier

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