Operation analyst Jobs in Aidlingen

Tätigkeitsbereich : Forschung & Entwicklung incl. DesignFachabteilung : Pattern RecognitionGesellschaft : Mercedes-Benz AGStandort : Mercedes-Benz Werk Sindelfingen, SindelfingenStartdatum : ..5Veröffentlichungsdatum : ..5Stellennummer : MER3LAOArbeitszeit : Vollzeit BewerbenAufgaben

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.

In Corporate Research & Development (RD), we shape future automotive generations with innovative, high-quality products and efficient processes. We are committed to developing highly automated driving systems for highways and urban areas, leveraging state-of-the-art technologies across our teams in Germany, India, China, and the US. To maintain our technological leadership and ensure exceptional customer experiences, we seek talented and dedicated Ph.D. students to join our automated driving (AD) development team in Sindelfingen.

Your Ph.D. will focus on developing a runtime monitoring system to ensure the safe operation of autonomous driving functions. This system aims to identify unknown operational points using vehicle sensor signals and planned behavior as inputs, based on empirical data from functional tests, to ensure safe and reliable operation post-deployment. Your research will involve creating a theoretical framework and practical methodologies to enhance the safety and reliability of autonomous driving systems. The primary objective is to devise a method that dynamically monitors system operation, detects failures, and triggers corrective actions to maintain safety, all while satisfying statistical guarantees.

You will work at the intersection of AD system development, system testing, and safety, under the supervision of experts in the field. During your research, you will utilize the latest machine learning techniques, such as out-of-distribution detection, conformal prediction, and statistical machine learning. You will have access to extensive data from system development and testing, including real and simulated recordings. Your findings will not only enhance the safety of high-level automated driving but also contribute significantly to the research community. We strongly encourage and support the publication of your work.

Responsibilities

• Assess the current state-of-the-art in runtime monitoring of safety-critical robotics applications including but not limited to automated driving
• Advance your knowledge in related fields like statistical machine learning, machine learning with guarantees, conformal prediction, and out-of-distribution detection
• Develop statistical models and algorithms to monitor system operation points
• Collect and analyze data from diverse test drives, including both real-world and simulated recordings
• Validate the efficacy of the developed models through available data, simulation and additional test drives
• Integrate the monitoring system into test vehicles
• Organize the recruiting and supervision of students to contribute to your research
• Document research findings and publish in scientific articles

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.

Qualifikationen

Preferred Qualifications

Additional information :

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

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

BenefitsEssens­zulagenMit­arbeiter­handy möglichMit­arbeiter­rabatte möglichMit­arbeiter­beteili­gung möglichMit­arbeiter EventsCoachingFlexible Arbeits­zeit möglichHybrides Arbeiten möglichGesund­heits­maß­nahmenBetrieb­liche Alters­ver­sorgungMobilitäts­angebote

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