Postdoctoral Research Associate, Scientific Machine Learning

About the role

We are seeking a Postdoctoral Research Associate with expertise in artificial intelligence (AI) and machine learning (ML) for multiscale physical systems. This position resides in the Computational Sciences and Engineering Division (CSED) at Oak Ridge National Laboratory (ORNL).

Major Duties/Responsibilities

• Research and development of scalable AI, ML, and deep learning models using the Oak Ridge Leadership Computing Facility (OLCF) systems.
• Conduct research with scalable transformer-based foundation models with large volumes of spatiotemporal physical data, with a focus on fluid and plasma dynamics.
• Collaborate within a multi-disciplinary research environment consisting of computational scientists, experimentalists, and engineers conducting basic and applied research in support of the Laboratory’s missions.
• Author peer-reviewed papers, technical papers, reports and proposals for internal and external release as well as represent the organization via technical presentations in workshops and conferences.

Basic Qualifications

• Ph.D. in Mechanical Engineering, Aerospace Engineering, Nuclear Engineering, Computational Science, or a field closely related to the job duties of this position.

Preferred Qualifications

• Strong background in scientific AI/ML, computational fluid dynamics, turbulence modeling, plasma physics, and edge/scrape-off layer transport.
• Demonstrated programming ability and knowledge of Python and/or C++.
• Experience with deep learning frameworks like PyTorch and application on high-performance computing (HPC) environments using distributed data or model parallel training.
• Experience with multi-physics simulations on HPC and with ML models.
• Experience working in a multi-disciplinary research environment.
• Demonstrated written and oral communication skills, a proven publication record, and effective interpersonal skills.

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 for up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and the availability of funding.