Working student position in the field of map learning from April 2025

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Number: MER0003JPO In Group Research & Mercedes-Benz Cars Development (RD), we design the automotive generations of the future. We are already working on vehicles that will secure the technology leadership of Mercedes-Benz in the future. Autonomous driving is one of the strategic areas of development at Mercedes-Benz. A central component for automated driving is a complete understanding of the vehicle environment. The environment detection is based primarily on sensors such as Lidar, camera and radar, but an important role is also played by a digital road map. The road map provides context information for evaluating the current situation, which can not be detected or can only be detected by the sensors and thus completes the environment detection. In addition, the road map provides important planning information about the route ahead. A road map for automated driving must be significantly more accurate, more complete, more current and safer than the map previously known in navigation systems. In order to achieve this, sensor data from the vehicles are used, among other things, to update the road map and to guarantee a correct HD card at any time via a closed loop process. In the “Map Learning” team, we are engaged in developing such a closed-loop process for creating high-precision maps. Your task focuses on the processing of the sensor data of our vehicle fleet for the production of highly accurate and detailed digital road maps. They develop scalable concepts and implement them together with our team. The focus is on the derivation of a consistent track model from the prepared sensor data using deep learning algorithms. These challenges include: Integration into existing software framework and state-of-the-art algorithms Concept expansion and training, as well as implementation of a learning-based approach to the generation of rich representations, especially from the field of deep learning, Graph Deep Learnings, Geometric Deep Learnings Developing and implementing an approach to assessing the required extent and ensuring the diversity of the training data used Conceptual preparation and development of an evaluation scheme to evaluate the results against an existing ground-Truth map and alternative approaches

Master's degree in Computer Science, Artificial Intelligence or Similar Secure knowledge of German and English in word and writing Safe handling of MS Office Embossed programming skills in Python Experience in dealing with ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) High level of initiative and team capacity Embossed communication skills Additional information: Of course, without formalities, we are not. Therefore, we ask you to apply exclusively online and to apply for your application a CV, current enrollment certificate with a specification of the semester, current grade, relevant certificates, if necessary. In addition, proof of compulsory internship and proof of the period of study (maximum size of the annexes 5 MB) and in the online form to mark your application documents as 'relevant for this application'. Further information on the setting criteria can be found here. Members of countries outside the European Economic Area may send their residence/work permit. We are particularly pleased to receive online applications of disabled people and disabled people. If you have any questions, you can also contact SBV-Sindelfingen@mercedes-benz.com to the site's severely disabled representative, who will be happy to assist you in the further application process after your application. Please understand that we no longer accept paper applications and there is no claim to return. Questions about the application process will be answered by HR Services by e-mail to myhrservice@mercedes-benz.com or by phone: 0711/17-99000 (Monday to Friday between 10-12 am and 13-15 pm).

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