Postdoc positions at CMU NREC

The US National Robotics Engineering Center is a part of the Robotics Institute of Carnegie Mellon University that specializes in research and development of complete robotics systems. Field testing is an important part of our approach to ensuring relevance of the work. Our activities span many application areas including mining, material handling, agriculture, hazardous, security, military, and space. See http://www.nrec.ri.cmu.edu/ for more information.

We would like to fill a number of postdoc positions immediately in support of new research programs.

3D Video: This area of research investigates algorithms to process range and appearance data in real-time in order to produce dynamic, highly realistic computer graphics models. Present applications include remote operation of robotic vehicles and the production of virtual models of extended environments. The ideal applicant would have demonstrated strengths in computer graphics and computer vision and be experienced in software development as a member of a small team.

Scene Understanding: This area of research investigates algorithms to process range and appearance data in real-time in order to produce terrain classifications of a complex outdoor environment that will be used to help guide an autonomous unmanned ground vehicle. Range and appearance data is available from two perspectives: 1) from lidar and image sensors on-board the robot and 2) from above the robot in the form of satellite imagery and fly-over lidar data. The ideal applicant would have demonstrated strengths in computer vision, 3D lidar data, scene understanding and/or obstacle classification, be experienced in software development as a member of a large team, and work with little supervision.

Terrain Characterization: This area of research investigates algorithms that will make use of proprioception sensor data, in real-time in an on-line learning manner, to improve the performance of an autonomous unmanned ground vehicle. Proprioception data is a measure of what the robot "feels" or "senses" as it drives over the terrain. Thus it is a natural source of feedback into an on-line learning mechanism to improve terrain classification (i.e. vegetation or rock) / characterization (i.e. measure of slip) which were predicted on the terrain ahead of the robot before the robot drove over the terrain. The ideal applicant would have demonstrated strengths in machine learning, and computer vision, be experienced in software development as a member of a large team, and work with little supervision.

Applicants should possess both a solid preparation for performing research and a strong interest in useful realization of the technology. Please address all applications and inquiries to Alonzo Kelly (alonzo@cmu.edu) .