Postdoctoral Research Associate, Scientific Machine Learning

Requisition Id16723

Overview:

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). CSED focuses on transdisciplinary computational science and analytics at scale to enable scientific discovery across the physical sciences, engineered systems, and biomedicine and health. It provides foundations and advances in quantum information sciences to enable quantum computers, devices, and networked systems. It develops community applications, data assets, and technologies and provides assurance to build knowledge and impact in novel, crosscut-science outcomes.

The selected candidate will join the Multiscale Materials (MsM) group within the Advanced Computing Methods for Physical Sciences Section in CSED. The MsM group is dedicated to delivering multiscale, multi-fidelity computational models, systems, and AI/ML tools using algorithms and analytics for materials and related physical sciences for a broad range of energy, transportation, and advanced manufacturing applications. In this role, the postdoctoral researcher will focus on developing foundation models for fluid and plasma dynamics, including aerodynamics, turbulence, and edge/scrape-off-layer transport. This will involve designing efficient and trustworthy ML algorithms for physical systems, scalable data loading and training methods for exascale computing platforms, and model verification and validation. The position offers an excellent opportunity to collaborate with multi-displinary domain scientists and advance large-scale scientific AI/ML models.

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.

Candidates are asked to submit a detailed cover letter describing their experience relative to the duties and qualifications described in this posting with their application.

Please submit three letters of reference when applying to this position. You can upload these directly to your application or have them sent View email address on click.appcast.io the position title and number referenced in the subject line.

Instructions to upload documents to your candidate profile:

• Login to your account via jobs.ornl.gov
• View Profile
• Under the My Documents section, select Add a Document

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.

If you have trouble applying for a position, please email View email address on click.appcast.io.

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.