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PhD – End-to-End Trainable System for Autonomous Driving with Introspection

Bosch Group

Full-time On-site Other Germany / BW / Renningen

Employer posted: October 30, 2026. Source checked: November 15, 2026 7:59 pm. Availability and final application requirements are confirmed on the employer’s site.

Source: Bosch Group — Germany careers. WorkerEra lists this opportunity from an external source; confirm current details with the employer.

About this opportunity

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!

Imported source listing. Details are supplied by the source and have not been independently verified by WorkerEra. Confirm the current vacancy on the employer’s page.

Source: Bosch Group — Germany careers

Employer details

Workplace address
Robert-Bosch-Campus 1, Renningen, BW, 71272, Germany

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  • Read the linked employer announcement and compare its requirements with your experience.
  • Tailor your CV to responsibilities and skills actually listed by the employer.
  • Confirm the work location, deadline and application documents before submitting.
  • Salary was not supplied in the listing fields; ask the employer about compensation.

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