Applied Artificial Intelligence & Autonomous Systems Postdoctoral Fellow
Job Overview
Employment Type
Temporary
Full-time
Compensation
Type:
Salary
Rate:
Range $82,692.00 - $91,308.00
Work Schedule
Flexible
Benefits
Health Insurance
Dental Insurance
Retirement Plan
Paid Time Off
Professional development opportunities
hybrid work schedule
Flexible working hours
Job Description
Lawrence Berkeley National Laboratory (Berkeley Lab) is a renowned scientific research institution managed by the University of California, committed to advancing cutting-edge technologies and fostering interdisciplinary innovation to address energy, environmental, and computing challenges globally. Established as a leader in scientific discovery, Berkeley Lab integrates world-class scientific expertise with state-of-the-art facilities to pioneer new approaches in energy analysis, environmental science, and high-performance computing applications. The laboratory emphasizes a collaborative culture grounded in its Stewardship Values: Team Science, Service, Trust, Innovation, and Respect. These values reinforce a commitment to equity and diversity, promoting an inclusive workplace where diverse perspectives fuel groundbreaking... Show More
Job Requirements
- Ph.D. degree in computer science, electrical engineering, mechanical engineering, robotics, or a closely related field
- Less than three years of paid postdoctoral experience
- Demonstrated research expertise in AI, reinforcement learning, and multi-agent systems
- Proficiency in Python and deep learning frameworks
- Ability to work independently and in collaboration with diverse teams
- Strong publication record
- Ability to meet application deadline and provide cover letter and CV
- Reside within 150 miles of Berkeley Lab for hybrid work
- Pass background check
Job Qualifications
- Ph.D. in computer science, electrical engineering, mechanical engineering, robotics, or related field
- Strong research background in artificial intelligence, reinforcement learning, and multi-agent systems
- Expertise in agent-based decision-making and safe, robust control methods
- Proven publication record in top-tier conferences or journals
- Proficiency in Python programming and deep learning frameworks such as PyTorch or TensorFlow
- Ability to work independently and collaboratively within a diverse research team
- Strong communication skills for presenting research and writing proposals
Job Duties
- Develop fine-tuning and Retrieval-Augmented Generation methods to augment large language models with transportation and electric grid knowledge
- Integrate fine-tuned large language models into existing agent-based simulation frameworks
- Design and develop multi-agent reinforcement learning algorithms with safety guarantees for autonomous vehicles and robotic systems
- Implement and validate safe and model-predictive control frameworks for autonomous trucking and warehouse management systems
- Conduct data analysis on pilot-scale deployments of vehicle-grid integration
- Develop control systems for smart charging and discharging in transportation and grid contexts
- Publish research findings in high-impact peer-reviewed conferences and journals
- Assist principal investigator and researchers in developing funding proposals
Job Qualifications
Experience
Entry Level (1-2 years)
Job Location
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