多智能体优化调度研究科学家_CR

Company Description

Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology - with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch.

Job Description

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

The ideal candidate will have strong expertise in the following, or closely related areas:

1. Software for manufacturing planning, e.g., flexible shop floor scheduling,
2. flexible manufacturing systems, e.g., robotic handling and transport or
3. automated logistics, particularly multi-agent path finding. The ideal candidate will posses both algorithmic and software engineering experience.

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.

Qualifications

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