Internship/ Master Thesis - Machine Learning and Control Systems - REF96929P

Continental is a leading tire manufacturer and industry specialist. Founded in 1871, the company generated sales of €19.7 billion in 2025 and currently employs around 78,000 people in 54 countries and markets.

Tire solutions from the Tires group sector make mobility safer, smarter, and more sustainable. Its premium portfolio encompasses car, truck, bus, two-wheel, and specialty tires as well as smart solutions and services for fleets and tire retailers. Continental has been delivering top performance for more than 150 years and is one of the world’s largest tire manufacturers. In fiscal 2025, the Tires group sector generated sales of 13.8 billion euros. Continental's tire division employs more than 56,000 people worldwide and has 19 production and 16 development sites.

Join the Machine Intelligence department at our location Hanover-Stöcken, where we bring cutting-edge AI and machine learning solutions to industrial tire machinery. Our work spans control systems, measurement technologies, extended reality, and robotics, creating a dynamic environment for innovation.

You will be part of an innovative team shaping the future of smart manufacturing by contributing to real-world industrial challenges and optimizing machines through data-driven solutions.

We are offering a master’s thesis position starting as soon as possible (July 2026). An internship is also possible in addition to the master’s thesis. A voluntary or mandatory internship is possible. Most tasks are performed on-site.

As an intern or master thesis student in Machine Intelligence, you will collaborate with a highly innovative team to explore and implement AI methodologies in control systems and develop digital twins that dynamically adapt to changing conditions in rubber manufacturing.

Your main tasks:

• Experiment with time-series and tabular machine learning models (e.g., for prediction, anomaly detection, and process optimization)
• Apply signal processing and other feature engineering techniques to improve model performance
• Develop adaptive control systems and machine learning models for real-world industrial challenges
• Deploy and industrialise models in production environments

• Master students (m/f/diverse) in the field of mechatronics, electrical engineering, mechanical engineering or similar
• Proficiency in Python and machine learning frameworks such as TensorFlow, PyTorch etc.
• Knowledge of programmable logic controllers (PLCs) and control systems
• Strong fundamentals in statistics
• Basic understanding of reinforcement learning
• Very good command of English and German language, written and spoken
• Teamwork, creativity, and interest in innovative technical solutions
• Strong communication skills and a structured approach to work

Please upload the following documents (mandatory):

• For mandatory internships: your study regulations, with the required duration clearly highlighted
• Current certificate of enrolment
• Current transcript of records

If applicable, please also upload:

• Valid residence permit
• Work permit including the supplementary sheet

Applications from severely handicapped people are welcome.

We offer the following benefits:

• International and dynamic work environment
• 100% member of our core team which exceeds classical intern tasks
• Flexible working hours to fit your study schedule
• A hybrid work environment – remote or on-site depending on team organization and tasks
• Attractive remuneration 
• Qualified support from our specialists
• Health-oriented workplace

If you are interested to learn more about this specific Continental location, you are welcome to visite our location page: Continental / Hannover Stöcken

Diversity, Inclusion & Belonging are important to us and make our company strong and successful. We offer equal opportunities to everyone - regardless of age, gender, nationality, cultural background, disability, religion, ideology or sexual orientation.

Ready to drive with Continental? Take the first step and fill in the online application.