Work environment

Volkswagen Group Innovation drives the development of future mobility concepts and advanced technologies for all brands of the Volkswagen Group worldwide. With the Innovation centers in Europe, Asia, and the United States, we leverage local expertise to develop solutions with both global and regional relevance. Working in an interdisciplinary environment, you will explore cutting-edge technologies, apply modern development methods in the automotive domain, and translate research insights into practical vehicle applications. As part of your PhD, your focus will be on long-term personalization for vehicle users. You will develop mechanisms that capture and analyze the dynamic customer context throughout the entire vehicle lifecycle and translate it into adaptive and transparent system behaviors. A particular challenge lies in integrating diverse input signals from observed user behavior to explicitly expressed preferences into consistent and continuously learning personalization strategies. We are looking for a proactive personality with an interest in responsible personalization, context-aware AI, and human-centric intelligent systems, who contributes to an interdisciplinary team and actively shapes the mobility of tomorrow. The primary place of work is our center in Wolfsburg. We look forward receiving your application! 

Possible Tasks within this Role

• Develop, analyze, and evaluate long‑term personalization mechanisms that adapt to changing customer behavior, vehicle context, and environmental factors
• Investigate methods for integrating heterogeneous input types (including behavior, car settings, explicit preferences, and direct customer request) into cohesive and adaptive personalization strategies
• Design user‑transparent and user‑controllable personalization interfaces, enabling customers to review, adjust, or delete personalization data in a trustworthy and predictable manner
• Research long‑term stability and consistency, including validation methodologies, and mechanisms for maintaining reliable personalization over extended vehicle usage
• Identify and evaluate responsible‑AI guardrails to ensure safety, predictability, and compliance when handling long‑term personalized data

The final doctoral topic is defined together with the professor. The prerequisite for the cooperation is the confirmation of supervision as well as a confirmed delimitation of topics by the professor of a university or research institution entitled to confer a doctorate.

Qualification requirements

• Good to very good university degree qualifying for doctoral studies in mathematics, physics, computer science, data science, electrical engineering or a related field
• Advanced proficiency in coding, including experience with data processing, optimization techniques, and software engineering best practices
• Deep understanding of AI/ML concepts, including generative models, agentic architectures, and multi‑modal systems
• Experience with applied AI research, such as model training/fine‑tuning, benchmarking, or developing experimental prototypes
• Strong analytical and problem‑solving abilities, ideally demonstrated through prior thesis work, publications, or research projects
• German language level A2 and English language level C1

Keywords

Adaptive Preferences, Transparent and User‑Controlled Systems, Model Stability, Multi‑Modal Input Fusion, Safety Guardrails