Work environment

In the area of longitudinal and lateral control functions, functions to increase performance and vehicle stabilization are being developed and adapted to the needs of Volkswagen customers. With the increasing number of innovative functions, the complexity and variety of variants in the integration, application and verification of functions is also increasing. In order to master complexity and develop efficiently, the use of artificial intelligence (AI) in the various development domains is to be systematically analysed, evaluated and implemented. We offer you the opportunity to gain experience as a PhD student in the field of chassis control systems and at the same time to work on highly relevant topics in the automotive industry. The doctoral work offers an exciting insight into the development of chassis control functions based on artificial intelligence. In our department, you will not only have the opportunity to work on groundbreaking projects, but also gain practical experience while working with the latest technology. Become part of a team that’s shaping the future by driving innovation in mobility and making a meaningful impact on people’s lives. Together, we’re building tomorrow, today.

Possible Tasks within this Role

• Developing hybrid machine learning methods to ensure Volkswagen specific driving control for assisted and automated driving
• Considering system influences such as component diversity, aging effects, and usage characteristics over time in adaptive driving control systems using ML techniques
• Providing and analyzing data from test drives and fleet operations for virtual system development
• Identifying potential for safe AI integration into current vehicle dynamics control systems and securing neural network outputs in safety-critical applications
• Defining a robust toolchain and algorithmic framework for virtual validation, including the consideration of external influences

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 with a doctorate qualification and a major in control engineering, automotive engineering, data science, or comparable courses of study

• Good programming skills in Python and Matlab

• Practical experience in the field of machine learning, especially reinforcement learning

• Practical experience with the design of controllers such as MPC (Model Predictive Control)

• Interest in scientific work and interdisciplinary cooperation

• High level of personal commitment as well as teamwork and communication skills

• Structured and reliable way of working

• German and English language level B2

Keywords

Large Language Models, Small Language Models, VLAM, Dissertation