The Mercedes-Benz Group AG is one of the most successful automotive companies in the world. Together with Mercedes-Benz AG, the vehicle manufacturer is one of the largest providers of premium and luxury cars and vans.

We are shaping the future of mobility at Mercedes-Benz by developing autonomous and automated driving systems for highways and urban areas. Our teams are inspired by current trends, find the best solutions for our customers, and develop the latest and greatest core technologies to meet these challenges.

Become part of an agile, innovative team that is passionately working to make our vehicles even safer and more autonomous. We are looking for a talented, energetic, and dedicated colleague (m/f/d) to join our Sensor Fusion team in the Böblingen area to research and actively advance the technologies of tomorrow.

These challenges await you:

• You will provide fundamental insights for our future Sensor Perception & Fusion system. This stack forms the basis for the environmental model, which describes other road users and infrastructure information, using camera, lidar, and radar data
• You will research new, data-driven multi-sensor perception & fusion algorithms and architectures utilizing transformer-based approaches to achieve optimal driving behavior
• You will monitor current research in the field of sensor perception & fusion, publish your own contributions, and present them at conferences
• You will document your results in reports and regularly present them to the Sensor Perception & Fusion teams

The requirement for hiring is the supervision of the PhD project by a university lecturer. The PhD student is responsible for choosing his/her/their own supervisor.

• Completed master's degree with excellent results in engineering, such as computer science, robotics, physics, or a related field
• Very good knowledge of state-of-the-art machine learning, such as transformer and attention concepts
• Good knowledge of probabilistically motivated sensor fusion algorithms
• Experience in applying these concepts to object perception and tracking based on multimodal sensor data such as radar, lidar, and camera
• Excellent and practical programming skills in Python
• Practical experience with Docker, Conda, Pytorch, and ML frameworks such as MMDetection
• C++ programming skills are an advantage
• Fluent English language skills

Personal Competencies:

• High level of discipline, initiative, independence and commitment
• Excellent communication and teamwork skills as part of a global team in a multicultural environment
• Ability to understand existing problems and methodically and creatively approach solution design
• Great willingness to learn and share knowledge, for example, by supervising students
• Openness to questioning the state of the art and high motivation to delve deeply into a topic

Additional information:

Would you like to write your doctoral dissertation in cooperation with Mercedes-Benz Group AG? We offer you an international network of experts, research materials, insights into our work and a personal mentor who will serve as your contact partner in addition to your doctoral advisor at your university.

Please apply exclusively online and mark your application documents as "relevant for this application" in the online form. Please find the criteria of employment "here". Citizens of countries outside the European Trade Union please send, if applicable, your residence / work permit.

We particularly welcome online applications from candidates with disabilities or similar impairments in direct response to this job advertisement. If you have any questions, you can contact the local disability officer once you have submitted your application form, who will gladly assist you in the onward application process: SBV-Sindelfingen@mercedes-benz.com

Please understand that we do not accept paper applications and that there is no right to get your documents returned.

If you have any questions regarding the application process, please contact HR Services by e-mail at myhrservice@mercedes-benz.com or by phone: 0711/17-99000 (Monday to Friday between 10 a.m. to 12 a.m. and 1 p.m. to 3 p.m.).