Postdoctoral Research Associate - AI for Science

Requisition Id 15489 

­­Overview:  

The Analytics and AI at Scale (AAIMS) group under Advanced Technology Section (ATS) of NCCS is hiring two postdoctoral research associates to push the frontier of agentic AI for science, scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models, in the context of leadership scientific workflows and applications. You’ll help design, train, and evaluate AI systems that plan, reason, and take actions to accelerate scientific discovery across domains (materials, chemistry, climate, fusion, biology, and more).

NCCS operates the Frontier exascale supercomputer and world-class data facilities. This role sits at the intersection of AI at scale and HPC, giving you unmatched resources to prototype new ideas, run large ablations, and translate methods into scientific impact. We are an inclusive dynamic environment that welcomes those with initiative and creativity.

Focus Areas:

• Agentic AI for Science: Autonomous and tool-using agents for experiment design, simulation steering, data collection, and lab/compute orchestration; planning and memory; multi-agent collaboration.
• Scientific Reasoning: Program/path-of-thought, tool-augmented and retrieval-augmented reasoning; uncertainty quantification and calibrated decisions.
• RL & Self-Improving Models: RLHF/RLAIF, online RL, self-play, open-ended discovery, reward modeling, curriculum/active learning, data selection, iterative post-training, safety alignment and guardrails.
• Foundation Models for Science at Scale: Pretraining, instruction tuning, continued pretraining, Mixture-of-Experts; distributed training/inference (e.g. FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation pipelines for reasoning and agents.
• Federated & Collaborative Learning: Cross-silo training across institutions and facilities; privacy-preserving learning (secure aggregation, differential privacy, MPC/HE); personalization under heterogeneity; governance-aware data/model sharing; collaborative evaluation
  
  

Major Duties and Responsibilities:

• Conduct and publish original research on AI for science at scale on leadership-class systems.
• Design, implement, and benchmark large-scale training and post-training pipelines (including distributed data/compute and evaluation harnesses).
• Collaborate with domain scientists and external partners; co-develop end-to-end AI workflows that demonstrably accelerate scientific discovery.
• Architect and operate federated and collaborative learning experiments spanning multiple sites (national labs, universities, industry) with secure aggregation, heterogeneity-aware scheduling, robust and efficient training and fine tuning.
• Contribute to open-source software, datasets, and standardized evaluation suites; mentor interns and students.
• Communicate results through papers, artifacts, and presentations at top-tier venues.
  
  

Basic Qualifications:

• Ph.D. in Computer Science, Computer Engineering, a physical/computational science discipline (e.g., physics, chemistry, materials science, climate, biology), or a closely related field (earned within the last 5 years or expected before the start date).
• Demonstrated research in at least one: agentic AI/LLM agents, scientific reasoning or tool-augmented LLMs, RL (RLHF/RLAIF/online RL), or foundation models for science,
• Software engineering skills (Python) and experience with modern DL stacks (PyTorch) and multi-GPU training.
• Evidence of ability to conduct independent research and publish in peer-reviewed venues.
  
  

Preferred Requirements:

• Publications in leading venues (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR/ICCV/ECCV, ACL/EMNLP, MLSys/SC/HPDC).
• Hands-on with distributed training/inference (FSDP, DeepSpeed, Megatron-LM), accelerator programming, and large-scale data pipelines.
• Experience building agents that use tools/APIs (e.g., code interpreters, simulation frameworks, databases, lab instruments) and evaluation for long-horizon tasks.
• Experience with RL and post-training (reward modeling, preference learning, offline/online RL, self-play, curriculum learning, data selection).
• Experience with federated learning (cross-silo FL, FedAvg/FedProx/personalization), privacy-preserving methods (secure aggregation, differential privacy) and collaborative training/evaluation across heterogeneous data and compute.
• Background in one or more scientific domains (materials, chemistry, climate, fusion, biology) and/or scientific software ecosystems.
• Strong communication skills and a collaborative mindset in cross-disciplinary teams.

Special Requirements: 

Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.

Letters of Recommendation: 

Please submit three letters of reference when applying to this position. You may upload these directly to your application or have them sent to Postdocrecruitment@ornl.gov with the position title and number referenced in the subject line.

Instructions to upload documents to your candidate profile:

• Login to your account via jobs.ornl.gov
• View Profile
• Under the My Documents section, select Add a Document

Security, Credentialing, and Eligibility Requirements:
For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required.  Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.

To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation.  This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year.  This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

For foreign national candidates:

If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment.  Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment.  

About ORNL:

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.

ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience.

Other benefits include the following: 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, and Employee Discounts.

If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: ORNLRecruiting@ornl.gov

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.

If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply.  UT-Battelle is an E-Verify employer.