Associate Research Scientist
 Columbia University | |
$77,000 -110,000
| |
 United States, New York, New York | |
| 535 West 116th Street (Show on map) | |
 Jan 26, 2025 | |
|
Columbia University in the City of New York: The Fu Foundation School of Engineering and Applied Science: Mechanical Engineering Location 01 Open Date Feb 06, 2024 Salary Range or Pay Grade $77,000 -110,000 Description Opening for an Associate Research Scientist position with Professor Matei Ciocarlie at the Department of Mechanical Engineering in the Fu Foundation School of Engineering & Applied Science at Columbia University.
The ROAM Lab is focused on robotics, with a particular emphasis on motor learning and complex skill development and applications in areas ranging from autonomous robotic manipulators to healthcare and rehabilitation robots. The successful candidate will conduct work on developing a full stack robotic manipulation platform for dexterous tasks in semi-structured environments, with the following responsibilities: * Research novel methods for understanding the fundamental interplay between hardware and sensorial capabilities that are possible using current technology, and the needs of learning-based control methods for producing robust control policies for complex motor skills such as dexterous manipulation. * Test the suitability of kinematic designs for robotic manipulators for specific dexterous tasks using motor learning in simulated environments. * Contribute to the mechatronic design and prototyping of robotic manipulators that are deemed appropriate for specific tasks. * Determine the relative importance of multiple sensing modalities (e.g. vision, touch, proprioception) for motor learning of complex manipulation tasks. * Integrate full-stack robotic systems comprising hardware, control and learning. Qualifications * PhD in a robotics-related engineering field (Mechanical Engineering, Computer Science, Electrical Engineering, etc.) * Extensive expertise integrating complex mechatronic systems comprising hardware, sensors, actuators, low-level firmware, control, communication, planning and learning. * Expertise with intrinsic sensing modalities for manipulation, such as touch and proprioceptive sensing. * Expertise with motor learning approaches, particularly focusing on the impact on hardware and sensorial capabilities. | |