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We are seeking an experienced Research Scientist to lead the optimization deployment and enhancement of a fleet of robotic systems in production environments .

The ideal candidate will have strong expertise in combinatorial optimization such as scheduling and task allocation robotic path planning (MAPF) and related learning-based methods.

Therefore we are seeking a candidate who is passionate about job scheduling multi-agent task allocation and path planning with a proven track record of designing and implementing innovative products and features.

This is a hands-on  role requiring deep and broad knowledge of software development tools and advanced algorithm development.

Key Responsibilities:

           Design and implement highly reliable embedded multi-agent task allocation and scheduling algorithms and validate designs through both simulation and real-world testing.

           Contribute to system  architecture decisions that shape the future of Boschs multi-agent dynamic orchestration system.

           Collaborate with cross-functional teamsincluding perception hardware and software expertsto deliver intelligent integrated systems and solutions.

           Travel as required to support on-site system testing.

Qualifications :

Basic Qualifications:

           PhD or Masters degree with 4 years of experience in Computer Science Computer Engineering Electrical and Computer Engineering Robotics Mathematics or a related field.

           Proficiency in Python/C or a related programming language.

           Demonstrated record of patents or publications in top-tier peer-reviewed conferences or journals.

           Experience in developing multi-agent task allocation and path planning algorithms for business applications.

           Proven ability to apply theoretical models in practical real-world environments.

           Proficiency in English for technical writing team and client communication.

Preferred Qualifications:

           PhD in Robotics Computer Science Mathematics or a related field.

           Experience developing and implementing data-driven approaches for multi-agent systems.

           Expertise in combinatorial optimization with applications in production line environments.

           Experience in production / manufacturing domain and related processes

           Experience in test-driven development and end-to-end testing of algorithms

Remote Work :

No

Employment Type :

Full-time