Postdoctoral Research Associate - AI for Lab Operations

Oak Ridge National Laboratory • Oak Ridge, TN

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About The Position

The Learning Systems Group in the Computer Science and Mathematics Division at Oak Ridge National Laboratory is seeking a postdoctoral researcher to lead research in generative AI, large language models, and human-in-the-loop agentic AI systems. The work targets Department of Energy operational challenges, including operational safety, predictive maintenance, automated hazard identification, and proactive risk mitigation. The researcher will develop, fine-tune, and evaluate generative AI architectures, multi-agent systems, and specialized software tools that analyze unstructured operational data, forecast hazardous conditions, and support human decision-making in high-consequence environments.

The role involves collaboration with machine learning researchers, software developers, and domain safety experts across the laboratory. Research may include practical operations tools, AI-driven risk modeling, multi-agent workflows for incident prevention, and trusted retrieval-augmented generation systems for safety compliance and real-time hazard evaluation. The position provides access to large-scale GPU computing resources and opportunities to publish and deploy AI solutions for national laboratory and DOE operational safety challenges.

The appointment is for up to 24 months, with the potential for extension. Initial appointments and extensions depend on performance and funding availability. Applicants must complete all Ph.D. requirements before starting and must be able to obtain and maintain the required federal credential or, where applicable, a favorable Local Site Specific Only risk determination.

Requirements

• Ph.D. in Computer Science, Artificial Intelligence/Machine Learning, Electrical/Computer Engineering, Applied Mathematics, or a closely related discipline; candidates expecting to complete all Ph.D. requirements by the end of 2026 are encouraged to apply.
• Applicants must not have received their Ph.D. more than five years before the application date and must complete all degree requirements before starting the appointment.
• Strong research background in machine learning and deep learning, with practical experience in generative AI, multi-agent AI systems, LLM fine-tuning, and AI tools for operational or text-heavy applications.
• Strong Python software engineering skills and proficiency with common ML libraries and frameworks such as Hugging Face, LangChain or LlamaIndex, scikit-learn, and pandas.
• Demonstrated research record, evidenced by peer-reviewed publications in leading AI/ML/NLP venues or technical tool development in complex domains.
• Strong oral and written communication skills.
• Ability to obtain and maintain an HSPD-12 PIV badge, or, for eligible foreign national candidates who have not met the U.S. residency requirement, obtain a favorable LSSO risk determination.
• Three letters of reference must be submitted with the application.

Nice To Haves

• Experience designing multi-agent AI orchestrations, agentic tool-use frameworks, vector databases, and enterprise-grade RAG systems for risk assessment or operational workflows.
• Direct experience in functional safety modeling, hazard identification, risk mitigation strategies, or reinforcement learning with human feedback for safety-critical applications.
• Experience building scalable AI operations software and deploying containerized ML pipelines on GPU clusters.
• Open-source contributions to AI/ML tools, experience working directly with domain operations teams, or collaborative R&D across national laboratories, industry, or academia.
• Experience using LaTeX to write scientific manuscripts.

Responsibilities

• Conduct original research and develop software tools in generative AI for operational safety, including hazard identification, incident trend analysis, and predictive risk mitigation.
• Architect, fine-tune, and deploy multi-agent AI systems, instruction-tuned LLMs, and retrieval-augmented generation pipelines for risk scoring, hazard reporting, and safety workflow orchestration.
• Develop validation frameworks, explainable AI methods, and safety-critical alignment techniques to support accurate, trustworthy, actionable decisions by human operators.
• Collaborate with computer scientists, AI researchers, and facility safety managers to integrate operations tools into laboratory safety management systems and risk mitigation protocols.
• Publish findings in peer-reviewed AI/ML conferences and safety engineering journals, release open-source tools, and contribute to research proposals.
• Uphold ORNL’s core values of Impact, Integrity, Teamwork, Safety, and Service.

Benefits

• Medical and retirement plans
• Flexible work hours
• On-site fitness, banking, and cafeteria facilities
• Prescription Drug Plan
• Dental Plan
• Vision Plan
• 401(k) Retirement Plan
• Contributory Pension Plan
• Life Insurance
• Disability Benefits
• Generous Vacation and Holidays
• Parental Leave
• Legal Insurance with Identity Theft Protection
• Employee Assistance Plan
• Flexible Spending Accounts
• Health Savings Accounts
• Wellness Programs
• Educational Assistance
• Relocation Assistance
• Employee Discounts

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree