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Oak Ridge National Laboratory

Postdoctoral Research Associate in AI for Science, Knowledge-Guided Machine Learning, and Scientific AI Workflows Oak Ridge National Laboratory in United States

Degree Level

Postdoc

Field of study

Computer Science

Funding

Two-year postdoctoral appointment with potential for extension, subject to performance and funding availability. ORNL offers competitive pay and benefits; no stipend amount is stated.

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Country

United States

University

Oak Ridge National Laboratory

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Keywords

Computer Science
Electrical Engineering
Information Technology
Deep Learning
Mathematics
Computational Science
High Performance Computing
Physics

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About this position

Oak Ridge National Laboratory (ORNL) is recruiting a Postdoctoral Research Associate in AI for Science within the Discrete Algorithms Group. This is a two-year postdoctoral position focused on artificial intelligence for science, knowledge-guided machine learning, scientific AI workflows, and scalable methods for discovery across DOE mission areas.

The research sits at the intersection of machine learning, high-performance computing, scientific modeling, and domain-informed AI. Topics mentioned in the call 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 HPC environments.

The successful candidate will design, implement, and evaluate AI methods that combine data-driven learning with scientific principles, constraints, ontologies, knowledge graphs, simulations, and experimental feedback. The role offers access to ORNL’s leadership-class computing ecosystem and collaboration with experts in machine learning, optimization, scientific computing, domain sciences, and HPC.

Eligibility highlights: a PhD in Computer Science, Applied Mathematics, Computational Science, or a related field; hands-on experience with HPC algorithms for scientific and ML models; and demonstrated research experience in AI/ML. Preferred background includes knowledge-guided or physics-informed ML, graph neural networks, geometric deep learning, operator learning, generative models, AI agents, MPI/NCCL, and matrix/tensor/graph algorithms.

Funding and appointment: the appointment is up to 24 months with possible extension, subject to performance and funding availability. ORNL notes competitive pay and benefits, but no stipend amount is listed.

Application: apply via the ORNL jobs portal. The posting requests three letters of reference, which may be uploaded or emailed to [email protected] with the position title and number in the subject line. The position remains open for a minimum of 5 days and closes when a qualified candidate is identified and/or hired.

Location: Oak Ridge, Tennessee, United States.

Funding details

Two-year postdoctoral appointment with potential for extension, subject to performance and funding availability. ORNL offers competitive pay and benefits; no stipend amount is stated.

What's required

A PhD in Computer Science, Applied Mathematics, Computational Science, or a related discipline is required. Applicants should have hands-on experience developing and applying HPC algorithms to scientific and machine learning models, plus demonstrated research experience with AI and ML techniques. Preferred experience includes knowledge-guided or physics-informed machine learning, scientific foundation models, graph neural networks, geometric deep learning, operator learning, generative models, AI agents, HPC matrix/tensor/graph algorithms, and distributed algorithms using MPI or NCCL. Applicants must have received their PhD no more than five years before the application date and must complete all degree requirements before starting.

How to apply

Apply through the ORNL jobs portal using the posting link. Submit three letters of reference with your application or have them emailed to [email protected] with the position title and number in the subject line. If you have trouble applying, contact [email protected].

More information can be found here

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