Cambridge Residency Program - Robotics
Working with researchers and engineers from diverse technical backgrounds, you will develop cutting-edge solutions for datacenter robotics and explore opportunities extending beyond this domain. Drive Innovative Research: Lead and collaborate on diverse projects to explore cutting-edge robotics technologies, addressing fundamental and applied research challenges.
Develop and Prototype: Design and implement novel algorithms and control strategies, and translate them into functional prototypes. Develop research code to automate and enhance hardware performance. Experiment and Validate: Plan, design, and execute experiments to test and validate prototypes in various real-world scenarios. Analyze data, refine models, and iterate designs to achieve optimal performance.
Collaborate and Disseminate: Work closely with a multidisciplinary team, fostering a collaborative research environment. Clearly communicate findings and progress through presentations, technical reports, and publications in high-impact academic venues PhD degree (or on-track to complete) in Computer Science, Robotics, Electronic Engineering, Mechanical Engineering, or a closely related field.
Demonstrated expertise in one or more areas of robotics, such as manipulation and grasping, kinematics, dynamics, motion control, and coordination. Hands-on experience with programming robots, developing control system algorithms, and integrating software with hardware.
Strong track record in relevant research, evidenced by publications in top-tier venues like ICRA, IEEE Robotics and Automation Letters, and IROS. Impact-driven mindset with a demonstrated ability to work and learn in a collaborative and diverse environment. Committed nature with effective communication skills. Proficiency in rapid and accurate development using programming languages such as C, C++, Python, or similar.
Experience with automation software, robotics simulation environments, or embedded systems programming. Experience with physical/embodied AI. Experience with learning based control.
