The design and development of innovative safety contracts will be your core focus establishing formal runtime interfaces to ensure safe interaction between decision-making and control in autonomous systems.
You will precisely define assumptions guarantees and adaptation rules for modular autonomy stacks to secure system integrity across different abstraction levels.
More over you will utilize advanced methods of optimal control sequential decision-making under uncertainty and reinforcement learning to develop robust high-performance solutions.
Using machine learning you will derive feasibility margins uncertainties and the safety contracts themselves from real-world data.
Translating research findings into concrete applications is a key goal particularly in the domain of vehicle motion control and autonomous driving to safely bridge the gap between high-level decisions and physical reality.
Last but not least you will contribute significantly to making learning-enabled autonomy modular by design guaranteeing safety by construction and enabling verification at runtime.
Qualifications :
Additional Information :
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 6
Need further information about the job
Felix Berkel (Functional Department)
49 1
Work #LikeABosch starts here: Apply now!
Remote Work :
No
Employment Type :
Full-time
PhD Safety Contracts for Physical AI • Böblingen, Baden-Wurttemberg, Germany
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