SLAS (Society for Laboratory Automation and Screening)
Data Scientist 3 - Robotics
SLAS (Society for Laboratory Automation and Screening), Richland, Washington, United States, 99352
The Data Sciences and Machine Intelligence group in ACMDD at PNNL seeks a Data Scientist to join the group to lead and support scientific research in robotics, autonomous systems, and intelligent control. This is an excellent opportunity to contribute to cutting-edge research in robotic autonomy, learning-enabled control, and embodied AI.
The primary focus of this senior scientist position will be to grow existing, and adding new, capabilities in the areas of Optimization, Robotics, and Artificial Intelligence, and to help strengthen the group’s leadership in data science and machine intelligence fields. A successful candidate should have shown significant national level expertise in one or more of the following technical areas: Design and integration of robotics systems, optimization and optimization-based decision-making, artificial intelligence and machine learning, autonomous control and decision systems, model predictive control, reinforcement learning algorithms, deploying machine learning using cloud and edge computing solutions, transformer architectures for time series analysis.
Responsibilities
Contribute to the development of intelligent, integrated robotic platforms for scientific applications such as autonomous laboratories. Develop and apply advanced algorithms for motion planning, learning-enabled control, and autonomous decision-making using techniques such as model predictive control (MPC), control barrier functions (CBFs), differentiable predictive control, and reinforcement learning. Explore cutting-edge topics such as robotic manipulation, Sim2Real transfer, vision-language-action models, and foundation models for robotics. Contribute to high-quality software development, including the use of physics-based simulators (e.g., MuJoCo, IsaacSim, Gazebo), ROS, and machine learning frameworks (e.g., PyTorch, JAX, TensorFlow). Qualifications
BS/BA and 5+ years of relevant work experience -OR- MS/MA and 3+ years of relevant work experience -OR- PhD with 1+ year of relevant experience. Preferred: PhD or MS in Robotics, Computer Science, Applied Mathematics, Electrical Engineering, Mechanical/Aerospace Engineering, or related scientific fields. Hands-on experience in robot learning, motion planning, navigation, and control using both classical and modern control methods (e.g., MPC, PID, LQR) and modern machine learning techniques (e.g., reinforcement learning, imitation learning, computer vision). This position is based at the PNNL main campus in Richland, WA and requires onsite work. Pacific Northwest National Laboratory (PNNL) is a world-class research institution powered by a highly educated, diverse workforce committed to the values of Integrity, Creativity, Collaboration, Impact, and Courage. PNNL is an Equal Employment Opportunity employer.
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Contribute to the development of intelligent, integrated robotic platforms for scientific applications such as autonomous laboratories. Develop and apply advanced algorithms for motion planning, learning-enabled control, and autonomous decision-making using techniques such as model predictive control (MPC), control barrier functions (CBFs), differentiable predictive control, and reinforcement learning. Explore cutting-edge topics such as robotic manipulation, Sim2Real transfer, vision-language-action models, and foundation models for robotics. Contribute to high-quality software development, including the use of physics-based simulators (e.g., MuJoCo, IsaacSim, Gazebo), ROS, and machine learning frameworks (e.g., PyTorch, JAX, TensorFlow). Qualifications
BS/BA and 5+ years of relevant work experience -OR- MS/MA and 3+ years of relevant work experience -OR- PhD with 1+ year of relevant experience. Preferred: PhD or MS in Robotics, Computer Science, Applied Mathematics, Electrical Engineering, Mechanical/Aerospace Engineering, or related scientific fields. Hands-on experience in robot learning, motion planning, navigation, and control using both classical and modern control methods (e.g., MPC, PID, LQR) and modern machine learning techniques (e.g., reinforcement learning, imitation learning, computer vision). This position is based at the PNNL main campus in Richland, WA and requires onsite work. Pacific Northwest National Laboratory (PNNL) is a world-class research institution powered by a highly educated, diverse workforce committed to the values of Integrity, Creativity, Collaboration, Impact, and Courage. PNNL is an Equal Employment Opportunity employer.
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