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Intern Vehicle Motion Prediction Algorithms

Auf Jobportal gelistet am 08. Okt. 2026

Im Job Fit analysieren

Mercedes-Benz AG

Sindelfingen

EnglischPraktikumPraktikumDeutsch von Vorteil

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Job Fit

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Gehalt (Schätzung)

Vollzeit-Referenzwert: 37.000 €–58.000 €/Jahr

Über diese Stelle

Life is always about becoming… Becoming means going on a journey to be the best version of our future selves. While we discover new things, we will face challenges, master them and grow beyond our individual limits.

Apply for a job at Mercedes-Benz and find your individual role and workspace to unleash your talents to the fullest. Empowered by visionary colleagues who share the same pioneering spirit. Joining us means becoming part of a global team that aims to build the most desirable cars in the world. Together for excellence.

Job-ID: MER00048I8

Tasks

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.

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

The activity can begin from November 2026.

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

Additional Information:

We look forward to receiving your online application, including a resume, cover letter, certificates, current certificate of enrollment stating your semester, proof of mandatory internship if applicable, and proof of the standard period of study. Please remember to mark your documents as "relevant for this application" in the online form and observe the maximum file size of 5 MB.

You can find further information on the hiring criteria here .

Severely disabled applicants and applicants with equivalent status are welcome! The representative for severely disabled employees ( ) will gladly support you in the application process.

People Solutions will be happy to help you with any questions you may have about the application process. You can reach us by email at or by phone at (Mon-Fri 10am-12pm & 1pm-3pm).

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

Mercedes-Benz Plant Sindelfingen, Sindelfingen

Bela-Barenyi-Str Tor 14

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Eckdaten der Stelle

Arbeitgeber: Mercedes-Benz AG
Position: Intern Vehicle Motion Prediction Algorithms
Stadt: Sindelfingen
Arbeitsmodell: nicht angegeben
Sprache: Englisch
Erfahrungsstufe: Praktikum
Auf Jobportal gelistet: 08. Okt. 2026
Quelle: StepStone

Bewertung des Unternehmens

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Intern Vehicle Motion Prediction Algorithms

Job Fit

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Tasks

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Additional Information:

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Eckdaten der Stelle

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