PhD - End-to-End Trainable System for Autonomous Driving with Introspection
Bosch · Renningen, BW, Germany
What the posting says
Each fact was read from this posting and checked:Statedin the posting, quotedConflictsposted two ways, both shownNot mentionedwe looked; it is not there
- Employment typeStated
- Full time
Not mentioned in this posting
We read the whole posting. These are not in it. Not a no: ask the recruiter, in your own words.
- PayNot mentioned
- The posting does not say
- Work arrangementNot mentioned
- The posting does not say
- ShiftNot mentioned
- The posting does not say
- ExperienceNot mentioned
- The posting does not say
- EducationNot mentioned
- The posting does not say
- Visa sponsorshipNot mentioned
- The posting does not say
Requirements, each with its sentence from the posting
These are the employer’s words. The index does not judge whether you meet them.
- No requirement has been itemized for this posting yet. The employer’s words are in the full posting below.
Read the full posting as the employer wrote it
Company Description
At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.
The Robert Bosch GmbH is looking forward to your application!
Job Description
The current state of the art in autonomous driving (AD), represented by Vision-Language-Action (VLA) models, has enabled vehicles to link perception with action in a more data-efficient way than classical End-to-End systems. Still, we are reaching the limits of VLAs: short-sightedness due to a lack of long-term prediction capabilities, no physical consistency, and limited coverage of long-tail scenarios. Therefore, another paradigm shift is required.
The aim of this research is to investigate the next generation of AD systems by moving from reactive VLA to predictive, agentic world models. The core thesis is that "imagination before action" is essential for safe and scalable autonomy.
- As part of our team, you will develop a framework for integrating VLA, world models, and agentic planners into a cohesive driving system.
- Furthermore, you will demonstrate improved performance in long-term prediction, edge-case handling, and verifiable safety through proactive simulation.
- You will drive disruptive change in how data is prepared and large systems are trained more efficiently by focusing on unexpected scenarios.
- Last but not least, you will contribute to the fundamental shift in the AD field from reactive control to imagination-based, agentic action, paving the way for more capable and trustworthy autonomous systems.
Qualifications
- Education: excellent university degree (Master's or equivalent) in Computer Science, Robotics, Electrical Engineering, Mathematics, or a related field
- Experience and Knowledge: profound knowledge of machine learning algorithms and principles, preferably deep learning; proven programming skills in Python and C++
- Personality and Working Practice: you are an open-minded team player who approaches challenges with a goal-oriented and logical mindset
- Languages: fluent in English (written and spoken), German is a plus
Additional Information
https://www.bosch-ai.com
www.bosch.com/research
The final PhD topic is subject to your university.
Start: according to prior agreement
Please submit all relevant documents (CV, certificates, and links to GitHub or kaggle account).
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.
Need support during your application?
Celina Dannecker (Human Resources)
+49 711 811 21346
Need further information about the job?
Thomas Michalke (Functional Department)
+49 711 811 43435
Work #LikeABosch starts here: Apply now!
The posting text is reproduced as the employer wrote it; the index adds nothing to it.