Applied Artificial Intelligence & Autonomous Systems Postdoctoral Fellow at Lawrence Berkeley National Laboratory

Berkeley, California, United States -

Full Time

Start Date

Immediate

Expiry Date

20 Mar, 26

Salary

7609.0

Posted On

20 Dec, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Artificial Intelligence, Autonomous Systems, Reinforcement Learning, Multi-Agent Systems, Control Methods, Data Analysis, Python, Deep Learning, Agent-Based Simulation, Model Development, Software Maintenance, Real-World Deployment, Safety Guarantees, Smart Charging, Vehicle-Grid Integration, Robotics

Industry

Research Services

Description

Lawrence Berkeley National Laboratory is hiring an Applied Artificial Intelligence & Autonomous Systems Postdoctoral Fellow within the Energy Analysis division. This position resides in the Systems and Energy Technologies Analysis Department and will develop AI-driven autonomous systems for next-generation transportation and grid applications. The role focuses on advanced control, LLM-based agentic frameworks, and robust decision-making to improve the safety and reliability of transportation and grid simulations, with contributions to model development, software maintenance, and real-world deployment. You Will: Develop fine-tuning and/or Retrieval-Augmented Generation (RAG) methods to augment LLMs with dedicated knowledge in transportation and electric grid domains. This involves designing methods to process input data and documents into appropriate structures for efficient training, establishing evaluation metrics to validate model performance, and improving the fine-tuning process with additional reinforcement learning steps. Integrate fine-tuned LLMs into the existing agent-based simulation framework in HEVI-LOAD to enable agentic scenario planning and parameter tuning. Design and develop novel multi-agent reinforcement learning algorithms with safety guarantees for connected autonomous vehicles/trucks, robotic and vehicle-grid integration systems, incorporating robust control methods and uncertainty quantification. Implement and validate safe and/or model-predictive control frameworks for real-time path planning and tracking of autonomous trucking and warehouse management systems. Conduct data analysis on pilot-scale deployments of vehicle-grid integration and develop control systems for smart charging/discharging within the context of both transportation and electric grid systems. Publish research findings in high-impact peer-reviewed conferences and journals, and present findings to prospective collaborators and funders. Assist the PI and other researchers in developing the funding proposals. We are looking for: Ph.D. in Computer Science, Electrical Engineering, Mechanical Engineering, Robotics, or a closely related field, with a strong research background in AI, reinforcement learning, and multi-agent systems. Demonstrated expertise in agent-based approaches and decision-making algorithms, such as safe and robust control methods, evidenced by publications in top-tier conferences or journals. Strong hands-on programming skills in Python and deep learning frameworks (PyTorch or TensorFlow), with experience in implementing and training complex neural network architectures. Ability to work as an independent researcher with a high level of scientific judgment and initiative, as well as effectively as part of a diverse collaborative team. Strong publication record in reinforcement learning, autonomous systems, robotics, or related fields. Desired skills/knowledge: Hands-on experience with robotics platforms and sim2real transfer (ROS, Carla, MetaDrive, or similar). Experience in vehicle-grid integration, autonomous vehicles, smart grid systems, or energy storage applications. Experience with vision-language models, large language models, or multi-modal learning systems. For consideration, please apply by Feb. 2, 2026 with the following application materials: Cover Letter - Describe your interest in this position and the relevance of your background. Curriculum Vitae (CV) or Resume. Additional information: Application date: Priority consideration will be given to candidates who apply by Feb 2, 2026. Applications will be accepted until the job posting is removed. Appointment type: This is a full-time 2 year, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. You must have less than 3 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree. Salary range: The monthly salary range for this position is $6,891 / mo - $7,609.00 / mo and is expected to start at $6,891 / mo or above. Postdoctoral positions are paid on a step schedule per union contract and salaries will be predetermined based on postdoctoral step rates. Each step represents one full year of completed post-Ph.D. postdoctoral and/or related research experience. Postdoctoral positions are paid on a step schedule per union contract and salaries will be predetermined based on postdoctoral step rates. Each step represents one full year of completed post-Ph.D. postdoctoral experience. Background check: This position is subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment. Work modality: This position is eligible for a hybrid work schedule - a combination of teleworking and performing work on site at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. Work schedules are dependent on business needs. Individuals working a hybrid schedule must reside within 150 miles of Berkeley Lab. A REAL ID or other acceptable form of identification is required to access Berkeley Lab sites (for more information click here). Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov Equal Employment Opportunity Employer: The foundation of Berkeley Lab is our Stewardship Values: Team Science, Service, Trust, Innovation, and Respect; and we strive to build community with these shared values and commitments. Berkeley Lab is an Equal Opportunity Employer. We heartily welcome applications from all who could contribute to the Lab's mission of leading scientific discovery, excellence, and professionalism. In support of our rich global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories under State and Federal law. Berkeley Lab is a University of California employer. It is the policy of the University of California to undertake affirmative action and anti-discrimination efforts, consistent with its obligations as a Federal and State contractor. Misconduct Disclosure Requirement: As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct, are currently being investigated for misconduct, left a position during an investigation for alleged misconduct, or have filed an appeal with a previous employer.

Responsibilities

The role involves developing AI-driven autonomous systems for transportation and grid applications, focusing on advanced control and decision-making. Responsibilities include model development, software maintenance, and real-world deployment.