PhD student Data & AI Driven Automotive SW Quality

Daimler Trucks North America LLC; Sindelfingen (Stadt); Vor Ort; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

A leading automotive company is seeking a PhD student in Data & AI Driven Automotive Software Quality. This role focuses on leveraging advanced AI techniques to enhance software quality assurance throughout the vehicle lifecycle. The ideal candidate has a Master's degree in Computer Science or related fields, programming skills in C/C++/Python, and a passion for automotive technology. This position offers the opportunity to work with an international network of experts and develop innovative solutions.

Leistungen

International network of experts

Research materials and insights

Personal mentor for guidance

Qualifikationen

• Master Degree or similar in Computer Science or IT.
• Background in AI/ML or data science.
• Experience with bug issue systems.
• Programming skills in C, C++, Python.

Aufgaben

• Develop AI-based solutions to improve software quality.
• Analyze and prioritize data sources for vehicle data.
• Propose data collection strategies for software quality.

Kenntnisse

AI/ML knowledge

Software engineering

Programming in C/C++/Python

Communication skills

Teamwork

Ausbildung

Master's degree in Computer Science or related

Tools

Git

Anomaly detection tools

Linux systems

Jobbeschreibung

Overview

Job Description - PhD student Data & AI Driven Automotive SW Quality (MER0003RSV)

In Mercedes-Benz Cars Research & Development (RD), we are shaping the future of the coming generations of Mercedes-Benz vehicles. We stand for the most innovative products of the highest quality and aspire to be leaders in the fields of electromobility and vehicle software. It is therefore our daily business to work on the technologies and vehicles of tomorrow.

Data, AI, and connected vehicles are at the forefront of our digital transformation with MB.OS at Mercedes-Benz. And we are looking for a highly motivated and talented PhD student to join our team and help us redefine the way we collect and work with vehicle data.

Objective: Join our mission to revolutionize software quality assurance at Mercedes-Benz by leveraging cutting-edge AI techniques and our flexible vehicle data collection infrastructure. This PhD position focuses on enhancing software quality through intelligent data-driven and AI-based methods.

Vision: Imagine a system that continuously scans diverse sources—ranging from internal bug tracking systems to public online forums—for software-related issues. It consolidates this information into a unified view of current problems and automatically triggers targeted data collection jobs to verify and quantify the reported issues. This proactive approach enables earlier detection of software quality issues and provides developers with a stronger, data-backed foundation for debugging and resolution.

Your Role

You will explore and develop AI-based solutions to continuously monitor and improve software quality across the vehicle lifecycle. Your work will span:

• Data Source Identification: Analyze and prioritize internal and external data sources such as development vehicles (CarLa, MB.OS Data Collection), production vehicles, test benches, defect tracking systems, public forums, and social media.
• Issue Analysis: Develop models to classify, consolidate and assess software issues (e.g., bug vs. feature, severity, frequency, root cause).
• Data Collection Strategy: Propose and validate new data collection strategies for software quality based on the findings.
• AI Application: Investigate where AI can be most effectively applied in the software quality pipeline and identify current limitations to its adoption.

Research Questions

• Where can AI deliver the most value in automotive software quality assurance?
• What are the current barriers to AI adoption in this domain?
• How can agent-based AI systems (Agent2Agent) be used to automate the software improvement loop?

Technologies & Methods

• Agentic AI (Agent2Agent architectures)
• Multimodal data integration (internal + external sources via MCP)
• Automated generation of MB.OS Data Collection jobs
• Natural language processing and anomaly detection

The Promotion can begin from November 2025.

Qualifications

• Master Degree or similar in Computer Science, Information Technology, Electrical Engineering or a comparable qualification
• Background in AI/ML, software engineering, or data science
• Passion for automotive software and quality
• Experience with bug issue systems, LLMs and agent technologies
• Knowledge of vehicle operating systems (Linux, QNX, AUTOSAR classic and adaptive) and embedded software development
• Programming skills in C, C++, Python and use of software-versioning tools (i.e. Git)
• Knowledge in data science or data analytics is helpful
• Business fluent English skills
• Self-management abilities
• Excellent Communication and Teamwork

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. 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: [e-mail]

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 [e-mail] or by phone: [phone] (Monday to Friday between 10 a.m. to 12 a.m. and 1 p.m. to 3 p.m.).