Intern Vehicle Motion Prediction Algorithms (m/f/d)

Your Responsibilities:

• Research and identify suitable prediction approaches, including kinematic and dynamic models, Kalman filters, regression techniques, data-driven methods, and hybrid approaches
• Implement prototypes in Python and integrate selected methods into C++ applications where appropriate
• Design and conduct reproducible benchmarking experiments
• Evaluate methods with regard to accuracy, robustness, parameterization effort, runtime performance, noise sensitivity, and real-time capability
• Document, visualize, and present your findings to the team

• Completed Bachelor's degree and current enrollment in a Master's program in Computer Science, Electrical Engineering, Robotics, Automotive Engineering, Mathematics, Data Science, or a comparable field
• Good written and spoken English skills; German language skills are an advantage
• Analytical thinking and a structured way of working
• Solid knowledge in at least one of the following areas: State estimation and sensor fusion
• Vehicle dynamics and motion models
• Signal processing
• Robotics or autonomous systems
• Programming experience in Python; experience with C++ is an advantage

Benefits

• Meal-Discounts
• Mobile Phone for Employees Possible
• Discounts for Employees Possible
• Annual Profit Share Possible
• Events for Employees
• Coaching
• Flextime Possible
• Hybrid Work Possible
• Health Benefits
• Company Retirement
• Mobility Offers
• Canteen, Café
• Inhouse Doctor
• Near-Site Childcare
• Parking
• Good Public Transport
• Barrier-Free Workplace

The Mercedes-Benz Augmented Reality Head-Up Display (ARHUD) requires reliable vehicle motion prediction to ensure accurate contact-analog visualizations, particularly considering the inherent latency of sensor-based estimates. In this internship, You will research, implement, and systematically evaluate different prediction methods. You will contribute to an applied automotive development project using real-world vehicle and sensor data.

The activity can begin from November 2026.