多智能体优化调度研究科学家_CR

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 Bosch’s multi-agent dynamic orchestration system.

•           Collaborate with cross-functional teams—including perception, hardware, and software experts—to deliver intelligent, integrated systems and solutions.

•           Travel as required to support on-site system testing.

Basic Qualifications:

•           PhD, or Master’s 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