R&D Associate - Robotics and Navigation

Oak Ridge National Laboratory•Oak Ridge, TN

About The Position

Oak Ridge National Laboratory (ORNL), in partnership with the University of Tennessee-Oak Ridge Innovation Institute (UT-ORII), is seeking an early-career engineer to conduct research and development in robotics and navigation for building construction. The work aims to reduce skilled-labor shortages, increase productivity, improve quality and safety, and lower costs while supporting affordable, energy-efficient, and resilient construction and retrofits. The successful candidate will be an ORNL staff member and UT-ORII Fellow, collaborating with ORNL and UT researchers and holding a Joint-Research Faculty appointment at UT. The role includes helping shape research programs and mentoring graduate and undergraduate students. The R&D Associate will develop autonomous exploration and mapping robotic platforms for GPS-denied, low-light, and geometrically repetitive environments. The work involves integrating heterogeneous sensors and onboard computing hardware; developing LiDAR-based SLAM, autonomous navigation, trajectory planning, and point-cloud mapping methods; and deploying and evaluating algorithms on physical mobile robots. The candidate will collaborate with researchers in robotics, surveying, remote sensing, and artificial intelligence, and communicate results through publications, reports, and presentations.

Requirements

Ph.D. in robotics, mechanical engineering, electrical engineering, computer engineering, computer science, aerospace engineering, or a closely related field.
Hands-on experience integrating sensors, actuators, computing hardware, or communications interfaces on a mobile robotic platform.
Research or development experience in one or more of automated controls, SLAM, autonomous navigation, or trajectory planning.
Experience integrating hardware, deploying algorithms on physical robots, and evaluating those algorithms.
Proficiency in Python, modern C++, and Linux-based development environments.

Nice To Haves

Experience with point-cloud processing and registration using Open3D or an equivalent library.
Experience with LiDAR-based or LiDAR-inertial SLAM, high-accuracy point-cloud mapping, or scan-to-scan registration.
Experience with the ROS 2 navigation stack, lifecycle-managed nodes, TF2, rosbag workflows, and distributed-system diagnostics.
Experience with CUDA and deployment on NVIDIA edge-GPU platforms such as Jetson Orin, Thor, or Spark.
Experience calibrating and synchronizing LiDAR, IMU, camera, and wheel-odometry systems.
Experience with underground, off-road, subterranean, or other unstructured-environment robotics and field testing.
Experience with Docker for containerization and deployment.
A strong publication record in robotics, controls, autonomous systems, sensor fusion, mapping, or a related area.
Excellent written and oral communication skills.
Ability to work independently and participate creatively in collaborative teams across the laboratory.
Ability to work in a fast-paced research environment, prioritize multiple tasks and deadlines, and adapt to changing needs.

Responsibilities

Conduct research on technologies that make new building construction and existing-building retrofits affordable, energy efficient, and resilient.
Design and implement a modular ROS 2 software architecture for onboard sensing, autonomy, mapping, data logging, launch management, and configuration management.
Develop and integrate ROS 2 drivers and hardware interfaces for LiDAR, IMUs, RGB or stereo cameras, wheel odometry, survey-grade laser scanners, and other sensing systems.
Develop procedures for sensor calibration, coordinate-frame management, timestamp synchronization, and data acquisition across heterogeneous devices.
Develop and evaluate multisensor localization and state-estimation methods that fuse LiDAR, IMU, camera, and wheel-odometry data in GPS-denied, low-light environments.
Implement LiDAR-based or LiDAR-inertial SLAM, factor-graph or pose-graph optimization, loop-closure validation, drift estimation, and drift correction for long trajectories in environments with limited or repetitive geometric features.
Develop autonomous navigation capabilities, including path planning, obstacle avoidance, autonomous exploration, frontier selection, and localization-aware trajectory planning.
Develop high-accuracy point-cloud registration and mapping workflows.
Plan and conduct experiments on physical robotic systems.
Establish quantitative measures for localization drift, mapping accuracy, and navigation reliability.
Publish results in peer-reviewed journals and conference proceedings, and communicate findings through technical reports and presentations.
Contribute ideas and help assemble proposals relevant to the UT-ORII Convergent Research Initiative.
Align behaviors, priorities, and interactions with ORNL's values of Impact, Integrity, Teamwork, Safety, and Service, and promote a respectful workplace and equal opportunity.