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Number: MER00045V7 The dissertation is located in the area MO360 Digital Factory Twin at Mercedes-Benz Manufacturing Engineering (MO/ET). Our team shapes the digital transformation of production planning and factory development by building the Digital Factory Twin as a central platform for planning, analysis and optimization of future production systems. A central challenge for digital twins is to continuously, accurately and automatically capture real factory environments. Despite significant advances in the fields of Computer Vision, Foundation Models and Generativer AI, the updating of industrial digital twins is still often based on manual processes or punctual measurements. For the factory of the future autonomous systems are needed, the production environments independently capture, detect changes and automatically transfer this information to digital models. New technologies such as autonomous drones, mobile robots, Vision Language Models (VLMs), multimodal foundation models as well as modern 3D reconstruction methods open up completely new possibilities. The doctorate is embedded in current research activities within the ARENA2036 and is in close cooperation with Mercedes-Benz Research and Development India (MBRDI). Together, you will explore innovative approaches at the interface of robotics, computer vision and generic AI to develop autonomous reality capture systems and self-refreshing digital twins for the factory of the future. Possible research questions (completed with university and Mercedes-Benz) How can autonomous drones and mobile robot systems capture production environments safely and efficiently? How can image, video, LiDAR and sensor data be automatically merged into consistent digital twins? What are the potentials of Vision Language Models (VLMs) and multimodal foundation models for understanding industrial environments? How can changes in factories be detected, classified and documented automatically? What methods enable the direct transfer of reality capture data into semantically enriched 3D models? How can security, data protection and governance requirements for autonomous data collection systems be taken into account? How can autonomous detection systems be seamlessly integrated into industrial digital and metaverse platforms? Expected scientific contribution Evaluation and prototypical implementation of autonomous reality capture methods based on real factory environments of Mercedes-Benz Combination of robotics, computer vision and generic AI for automated recording and interpretation of production areas Scientific findings for self-updating digital twins deriving concrete added value for planning, operation and optimization of future production systems The final design of the promotional theme is done in close coordination between you, the university and Mercedes-Benz. The activity can begin from mid-October 2026. Adjustment requirement is the support of the doctoral project by a university lecturer. The selection of a corresponding person is the responsibility of the doctoral student.

Special conditions Master's degree in Computer Science, Robotics, Electrical Engineering, Computer Engineering, Mechatronics, Data Science, Computational Engineering or a comparable degree course Very good knowledge of Python First experience with machine learning frameworks such as PyTorch, TensorFlow or comparable technologies First practical experience in dealing with Generative AI models, Vision Language models or multimodal foundation models Interest in the areas of autonomous systems, computer vision, robotics and digital twins Required knowledge Digital twins Reality Capture Technologies LiDAR and sensor data processing SLAM (Simultaneous Localization and Mapping) Gaussian Splating or Neural Rendering 3D reconstruction Computer Vision Deep Learning Robotics and autonomous navigation NVIDIA Omniverse Other skills Ability to abstract complex technical questions scientifically Embossed analytical and conceptual way of thinking Self-employed and structured work Enthusiasm for research at the interface of AI, robotics and industrial application Very good English skills in word and writing Team skills and joy in interdisciplinary cooperation Additional information: Doctorate in the Mercedes-Benz Group! Benefit from our know-how, an international expertise network, research materials, work insights and personal support by mentors – in addition to your university. Further information can be found here. We look forward to your online application with CV, Letters and Certificates. Please do not forget to mark your documents as ''relevant for this application' in the online form and note the maximum file size of 5 MB. Disabled and equalized applicants are welcome! The severely disabled representative (sbv-sindelfingen@mercedes-benz.com) is happy to support you in the application process. HR Services will be happy to help you with questions about the application process. You can reach us by email via myhrservice@mercedes-benz.com or by phone at 0711/17-99000 (Mo-Fr 10-12am & 13-15am).

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