Postdoctoral Research Associate -AI for Science

Oak Ridge National Laboratory; Oak Ridge (TN); On-site; USD 60,000 - 76,000; Full time

Benefits offered by this job

Relocation Assistance

Educational Assistance

Wellness Programs

Flexible Work Hours

Job summary

Oak Ridge National Laboratory seeks a Postdoctoral Research Associate specializing in AI for science. You will research at the intersection of ML, HPC, scientific modeling, and domain-informed AI to accelerate discovery across DOE mission areas in energy, materials, biology, and more.

The role involves designing and evaluating AI methods that couple data, simulations, and scientific principles, with opportunities to work on world-class computing resources and scalable AI workflows for

Qualifications

• PhD required in CS/Math/Computational Science.
• Hands-on experience with HPC algorithms for ML models.
• Proven research experience in AI/ML techniques.

Responsibilities

• Develop AI and knowledge-guided learning algorithms for scientific discovery.
• Design AI workflows integrating simulation, data, domain knowledge and HPC.
• Incorporate scientific constraints, ontologies, knowledge graphs into ML systems.
• Explore scientific foundation models, surrogate models and digital twins.

Skills

HPC algorithms

AI/ML techniques

Knowledge-guided ML

Physics-informed ML

Graph neural networks

Geometric deep learning

Operator learning

Generative models

AI agents

High-performance computing apps

Education

PhD in Computer Science / Applied Mathematics / Computational Science

Tools

MPI

NCCL

Distributed algorithms

Job description

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Postdoctoral Research Associate -AI for Science

Oak Ridge National Laboratory is the largest US Department of Energy science and energy Laboratory, conducting basic and applied research to deliver transformative solutions to compelling problems in energy and security.

The Discrete Algorithms Group at Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral researcher for a two-year position specializing in artificial intelligence for science, knowledge-guided machine learning, and scalable scientific AI workflows. The successful candidate will conduct research at the intersection of machine learning, high-performance computing, scientific modeling, and domain-informed AI to accelerate discovery across DOE mission areas such as energy, materials, biology, nuclear science, autonomous laboratories, and scientific computing.

This position is motivated by emerging national priorities in AI for science, including the DOE Genesis Mission: transforming science and energy through AI-enabled research and development workflows. The successful candidate will develop novel AI methods that integrate scientific knowledge, simulation, experimental data, and large-scale computing to achieve measurable AI advantage in scientific discovery. Research directions may include scientific foundation models, physics- and knowledge-guided machine learning, graph and geometric learning, surrogate and reduced-order modeling, uncertainty-aware AI, autonomous experimentation, scientific agents, and scalable AI workflows for heterogeneous high-performance computing environments.

The successful candidate will design, implement, and evaluate AI methods that couple data-driven learning with scientific principles, constraints, ontologies, knowledge graphs, simulations, and experimental feedback. This role offers an exceptional opportunity to pursue an ambitious research agenda that advances trustworthy, interpretable, and scalable AI for science while collaborating with leading experts in machine learning, optimization, scientific computing, domain sciences, and high-performance computing. The candidate will have opportunities to work with world-class computing resources, including ORNL’s leadership-class computing ecosystem, and to contribute to AI-enabled scientific workflows that address high-impact national challenges.

Responsibilities include, but not limited to:

• Develop novel AI, machine learning, and knowledge-guided learning algorithms for scientific discovery and DOE mission applications..
• Design scientific AI workflows that integrate simulation, experimental data, domain knowledge, and high-performance computing..
• Develop methods for incorporating scientific constraints, conservation laws, mechanistic models, knowledge graphs, ontologies, and expert knowledge into machine learning systems.
• Investigate scientific foundation models, surrogate models, digital twins for different scientific disciplines

Basic Qualifications:

• A PhD in Computer Science, Applied Mathematics, Computational Science, or related discipline.
• Demonstrated hands-on experience and understanding of developing and applying HPC algorithms to scientific and ML models.
• Demonstrated research experience with AI and ML techniques.

Preferred Qualifications:

• Experience with knowledge-guided machine learning, physics-informed machine learning, scientific foundation models, graph neural networks, geometric deep learning, operator learning, generative models, or AI agents.
• Knowledge of HPC matrix, tensor and graph algorithms.
• Knowledge on distributed algorithms using MPI and other frameworks such as NCCL.
• Knowledge of high-performance computing and its applications.

Special Requirements:

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.

• 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.

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.

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.