PhD Intern- Autonomous Intelligence

Pacific Northwest National Laboratory • Richland, WA

6h

About The Position

At Pacific Northwest National Laboratory (PNNL), our core capabilities are organized into major departments called Directorates, each focused on a specific area of scientific research or function, with its own leadership team and dedicated budget.
Our Science & Technology Directorates include:
National Security
Earth and Biological Sciences
Physical and Computational Sciences
Energy and Environment
Additionally, we host the Environmental Molecular Sciences Laboratory, a DOE Office of Science user facility located on the PNNL campus.

The Physical and Computational Sciences Directorate (PCSD) combines strengths in experimental, computational, and theoretical chemistry and materials science with advanced computing, applied mathematics, and data science capabilities. These resources are central to PNNL’s discovery mission. Our greatest asset is our people—experts across diverse scientific disciplines who collaborate to tackle the most significant scientific challenges of our time.

Within PCSD, the Advanced Computing, Mathematics, and Data Division (ACMDD) conducts basic and applied research in artificial intelligence, applied mathematics, computing technologies, and data and computational engineering. Our teams apply end-to-end co-design principles to advance energy-efficient computing systems and develop next-generation algorithms to analyze, model, and control complex systems in science, energy, and national security.

Requirements

• Candidates must be currently enrolled/matriculated in a PhD program at an accredited college.
• Minimum GPA of 3.0 is required.

Nice To Haves

• Currently pursuing a PhD in Robotics, Computer Science, Data Science (Generative AI focus), or a related technical field.
• Experience with robotic manipulation and control, including grasp synthesis, inverse kinematics, calibration, and contact-rich control methods.
• Familiarity with low-level robot interfaces for torque, velocity, and position control.
• Practical knowledge of robot learning techniques such as imitation learning, reinforcement learning, model predictive control, diffusion policies, or fine-tuning vision-language-action models.
• Understanding of trajectory optimization and motion planning frameworks for navigation and manipulation (e.g., Nav2, OMPL, MoveIt 2).
• Experience with computer vision and 3D perception, including object pose estimation and point-cloud processing.
• Proficiency in ROS 2 and integration with simulation environments such as Isaac Sim, Gazebo, MuJoCo, or PyBullet.
• Ability to build autonomous workflows using frameworks like LangGraph or LangChain.
• Strong skills in system integration and architecture for real-time applications.
• Solid Python programming and Git-based collaboration skills.
• Bonus: Experience with tactile sensing, mobile manipulation, or exposure to scientific experimentation and lab automation.

Responsibilities

• Conduct theoretical and research-driven investigations into advanced AI and autonomous systems.
• Develop and analyze novel algorithms, architectures, and mathematical models for autonomous intelligence.
• Explore foundational concepts in graph representation learning, graph neural networks, and knowledge graph construction.
• Advance research in scientific machine learning, semantic reasoning, and large-scale AI models.
• Perform conceptual modeling, simulation studies, and algorithmic proofs to validate theoretical approaches.
• Contribute to peer-reviewed publications, technical reports, and presentations for scientific audiences.
• Collaborate with multidisciplinary teams to integrate theoretical insights into broader research initiatives.

Benefits

• Employees are offered an employee assistance program and business travel insurance.
• Employees are eligible for the company funded pension plan and 401k savings plan, once eligibility requirements are met.

What This Job Offers

• Career Level: Intern
• Education Level: Ph.D. or professional degree