Postdoctoral Research Associate - Workflow Systems Group

Postdoctoral Research Associate - Workflow Systems Group

The Data and AI Systems Research Section/Workflow systems Group within the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral researcher with expertise in data management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL, you will collaborate with a dynamic team of scientists and engineers, leveraging cutting-edge resources;

most notably the Frontier supercomputer, the world's first exascale computing system. This is a unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum.

In this role, you will have the opportunity to lead and contribute to cutting-edge research aimed at transforming scientific data management and workflows to enable AI-readiness  will work on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed optimization of machine learning workflows. Collaborating with world-class scientists, you will enhance your expertise in resource optimization, scalable computing techniques, fault resilience, and advanced AI applications.

This role offers unparalleled access to ORNL’s world-leading computational resources, including the Frontier supercomputer, and the chance to make meaningful contributions to DOE's mission-critical scientific domains.

Roles and responsibilities include, but not limited to, one or more of the following:

• Design and implement system software to enable AI-readiness for scientific data by developing adaptive techniques capable of maintaining relationships between data and metadata.
• Collaborate on innovative solutions to automate and optimize the interplay between large scientific simulations, data ingestion, and AI processes (e.g., model training, inference).
• Develop agentic AI systems and AI harnessing techniques to enhance model quality, resource optimization, and adaptive execution in diverse workflows.
• Investigate strategies to balance performance and resilience across heterogeneous computational resources while addressing workflow requirements for scientific applications.
• Validate distributed intelligence algorithms at scale on ORNL's computational resources, including the Frontier supercomputer, addressing critical challenges in science and engineering.
• Communicate and coordinate experimental results with other domain experts to facilitate collaboration.
• Present and report research results and publish scientific results in peer-reviewed journals or conferences.

Basic Qualifications:

• A PhD in Computer Science, Applied Mathematics, Computational Science, or related discipline completed within the last five years.
• An excellent record of productive and creative research as demonstrated by publications in top peer-reviewed journals and conferences
• Demonstrated research experience with HPC, AI/ML and/or distributed systems techniques.
• Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and parallel computing.
• Demonstrated hands-on experience and understanding of developing scientific data management, workflows and resource management problems.
• Strong problem-solving and communication skills, with the ability to work collaboratively in a team setting.

Preferred Qualifications:

• Experience with AI-readiness pipelines, particularly in integrating scientific data with AI workflows.
• Familiarity with concepts like metadata management, parameter space coverage, or agentic AI.
• Prior exposure to DOE workflows or national laboratory environments
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative and frequently interacting teams of researchers.

Special Requirements:

Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree…