Postdoctoral Research Associate, Scalable AI and Computational Imaging

Postdoctoral Research Associate, Scalable AI and Computational Imaging

Requisition

Oak Ridge National Laboratory (ORNL), the U.S. Department of Energy’s largest multi-program science and energy laboratory, has an extraordinary 80‑year history of tackling the nation’s most challenging scientific problems. With a dedicated staff of over 6,000 people, ORNL advances breakthrough research in energy, environment, computing, and national security.

The Multiscale Biomedical Systems Group within the Advanced Computing in Health (ACH) Section of the Computational Sciences and Engineering Division (CSED) is seeking two Postdoctoral Research Associates to join our growing research team. These positions focus on developing next‑generation AI and high‑performance computing (HPC) methods for computational imaging and spatiotemporal data analysis.

We are especially interested in candidates with strong technical expertise in AI architecture design (e.g., Vision Transformers, foundation models, and federated learning), scalable computing on leadership‑class supercomputers, and computational imaging.

• Vision Transformers and foundation models for scientific and biomedical imaging
• Federated and distributed learning on large‑scale HPC systems
• Scalable and energy‑efficient AI training algorithms
• Image reconstruction, segmentation, and spatiotemporal modeling
• High‑performance computing for large‑scale AI and scientific data

• Design and implement advanced AI architectures and workflows for imaging and spatiotemporal data.
• Develop efficient and scalable training algorithms for deployment on HPC systems such as Frontier.
• Collaborate with interdisciplinary teams across ORNL and partner institutions.
• Lead and contribute to peer‑reviewed publications, technical reports, and conference presentations.
• Support proposal development for new research initiatives.

• A Ph.D. in computer science/engineering, electrical engineering, data science or a related field completed within the last five years.
• Experience with AI and efficient computing.
• Strong programming skills.
• Familiarity with popular deep‑learning platforms such as PyTorch and Tensor Flow.

• Expertise in vision transformers or large vision AI models.
• Expertise in high‑performance computing.
• Expertise in image and spatiotemporal data processing.
• Expertise in federated learning on large computing clusters.
• A strong publication record in peer‑reviewed journals and conferences.
• Excellent written and oral communication skills.
• Motivated self‑starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory.
• Ability to function well in a fast‑paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever‑changing needs.

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 ‑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.

New employees must successfully complete and pass a Federal Tier 1 background check investigation, including a declaration of illegal drug activities within the last year. 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 will need to obtain a favorable Local Site Specific Only (LSSO) risk determination…