Data Scientist 2 - Robotics

Pacific Northwest National Laboratory•Richland, WA

1d•Onsite

About The Position

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. You will join a multi-disciplinary team advancing scientific discovery through intelligent systems and autonomous laboratories, while helping develop new capabilities in optimization, machine learning, and integration. 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. As a researcher in robotics and autonomous systems at PNNL, you will contribute to the development of intelligent, integrated robotic platforms for scientific applications such as autonomous laboratories. Your work will support PNNL’s mission to accelerate scientific discovery through automation, modeling, and machine intelligence. You will contribute to software development and applied mathematics research in robotics for scientific applications such as autonomous laboratories. The emphasis will be given to modeling, simulation, system integration, and control of heterogeneous robotics systems and multiagent systems. You will 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. You will also explore cutting-edge topics such as robotic manipulation, Sim2Real transfer, vision-language-action models, and foundation models for robotics, helping to drive robust task learning and generalization across physical and simulated platforms. A key part of your role will involve contributing 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). You will help design and maintain codebases that support scalable experimentation, dataset acquisition, and integration of ML-enabled control systems in both simulated and physical testbeds. You will also be expected to summarize technical findings, prepare and contribute to peer-reviewed publications, and present results in leading conferences and journals. Collaboration is central to this role. You will work closely with a diverse team of computer scientists, engineers, mathematicians, and domain experts. You are also expected to mentor post-doctoral researchers, and seek programmatic funding. As such, excellent communication and interpersonal skills are essential for engaging in interdisciplinary research and delivering impactful scientific outcomes. Designs, develops, and implements methods, processes, and systems to analyze diverse data. Applies knowledge of statistics, machine learning, advanced mathematics, simulation, software development, and data modeling to integrate and clean data, recognize patterns, address uncertainty, pose questions, and make discoveries from structured and/or unstructured data. Produces solutions driven by exploratory data analysis from complex and high-dimensional datasets. Designs, develops, and evaluates predictive models and advanced algorithms that lead to optimal value extraction from the data. Demonstrates ability to transfer skills across application domains. This position is based at the PNNL main campus in Richland, WA and requires onsite work.

Requirements

BS/BA and 2 years of relevant experience -OR- MS/MA -OR- PhD

Nice To Haves

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).
Experience leading or contributing to R&D proposals for federal agencies (e.g., DOE, DARPA, NSF), with a history of successful funding as PI or co-PI.
Deep understanding of robot kinematics, dynamics, and sensor integration and perception pipelines.
Familiarity with multimodal perception and embodied AI for safe context-aware autonomous decision-making.
Experience with physics-simulation frameworks such as MuJoCo, Gazebo, and IsaacSim.
Strong programming skills in Python/Julia/C++, ROS, and machine learning frameworks (e.g., PyTorch, JAX, TensorFlow).
Experience working with robotic systems such as manipulators, mobile robots, autonomous vehicles, or similar platforms.
Experience using machine learning in cloud environments (such as Google Cloud and AWS) and edge hardware for real-time deployment and scalability.
Experience with modern scientific deep learning methods (e.g., Neural ODEs, PINNs, Operator networks, Hamiltonian and Lagrangian neural networks, graph neural networks).
The ability to develop and evaluate integrated systems.
Demonstrated leadership in delivering complex, end-to-end robotics solutions with successful Sim2Real transfer for various tasks and automated workflows.
Prior experience mentoring early career staff, guiding multidisciplinary teams, and shaping research vision and technical roadmaps.
Proven track record of impactful results, evidenced by successful projects, fellowships, grants, patents, and publications in top robotics and AI conferences/journals (e.g., ICRA, RSS, IROS, CoRL, ACC, CDC, NeurIPS, ICML, CVPR).
Ability to architect and evaluate integrated robotic systems across simulation and real-world environments.

Responsibilities

Designs, develops, and implements methods, processes, and systems to analyze diverse data.
Applies knowledge of statistics, machine learning, advanced mathematics, simulation, software development, and data modeling to integrate and clean data, recognize patterns, address uncertainty, pose questions, and make discoveries from structured and/or unstructured data.
Produces solutions driven by exploratory data analysis from complex and high-dimensional datasets.
Designs, develops, and evaluates predictive models and advanced algorithms that lead to optimal value extraction from the data.
Demonstrates ability to transfer skills across application domains.
Contribute to the development of intelligent, integrated robotic platforms for scientific applications such as autonomous laboratories.
Support PNNL’s mission to accelerate scientific discovery through automation, modeling, and machine intelligence.
Contribute to software development and applied mathematics research in robotics 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, helping to drive robust task learning and generalization across physical and simulated platforms.
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).
Design and maintain codebases that support scalable experimentation, dataset acquisition, and integration of ML-enabled control systems in both simulated and physical testbeds.
Summarize technical findings, prepare and contribute to peer-reviewed publications, and present results in leading conferences and journals.
Work closely with a diverse team of computer scientists, engineers, mathematicians, and domain experts.
Mentor post-doctoral researchers.
Seek programmatic funding.