OVERVIEW:

The Workflow Systems Group in the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is seeking a staff fellow with expertise in machine learning and high performance computing to help develop high-fidelity computational tools that are used for large-scale, physics-based simulations of fusion energy systems in partnership with the University of Tennessee-Oak Ridge Innovation Institute (UT-ORII). As part of the UT-ORII Convergent Research Initiative on Accelerating Fusion Technology and as an ORNL UT-ORII staff fellow, successful applicants will be part of a team of Research Faculty and ORNL staff to collaborate in the area of Fusion Materials.
In this role, you will work with groups of scientists across ORNL and the University of Tennessee (UT) to develop and apply physics-informed AI surrogate models for scientific simulations to accelerate research in fusion energy systems. This includes developing new machine learning models and software for scale-bridging and physics coupling in multiscale and multiphysics simulation codes for materials science and plasma-material interactions in fusion energy systems. You will also advance knowledge of key AI methods such as deep learning, operator learning, and Bayesian optimization, and apply it to develop next-generation surrogate models. This position will enable you to coordinate and conduct research and experiments related to the performance and reliability of AI surrogate models as well as develop mechanistic understanding and models for relations among simulation parameters, AI models, and predictive performance.
As a UT-ORII fellow, your career will develop in collaboration with researchers from both UT and ORNL through this early-career position primarily located at ORNL and with a Joint-Research Faculty (JFO) appointment at UT. As an integral part of the team, you will engage in a dynamic blend of activities, from advanced AI model developments and AI deployments to accelerate fusion material development to crafting comprehensive reports and impactful proposals. In addition to helping shape research programs, mentorship will be a key aspect of your role, guiding and inspiring graduate and undergraduate students. As a valued, emerging researcher in the ORNL Computing and Computational Sciences Directorate and the UT-OR Innovation Institute, you’ll have access to a rich network of resources, including seminars, training opportunities, and collaborations that will propel your career forward.
UT-ORII was founded in 2019 by the University of Tennessee and Oak Ridge National Laboratory to help the US maintain prominence as a global leader in innovation and discovery, and to create a robust talent pipeline in areas of growing national need and demand. UT-ORII is funded by the Department of Energy and the State of Tennessee.

BASIC QUALIFICATIONS:

• A PhD in Computer Science, Applied Mathematics, Computational Science, Materials Science, Nuclear Engineering, or a related discipline.
• Demonstrated research experience with AI and machine learning techniques, particularly in scientific applications.
• Demonstrated hands-on experience and understanding of developing and applying AI-based surrogate models.
• Excellent written and oral communication skills and the ability to communicate in English to an international scientific audience.

PREFERRED QUALIFICATIONS:

• Knowledge of high-performance computing and its application to AI model training and deployment.
• Knowledge of surrogate modeling techniques and their application in scientific research.
• Knowledge of design and analysis of complex systems using AI.
• Knowledge of data management, high performance I/O, and data compression methods.
• Experience working in a cross-discipline team with other modelers and experimentalists.
• An excellent record of productive and creative research as demonstrated by publications in peer-reviewed journals.
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative and frequently interacting teams of researchers.
• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever-changing needs.

SPECIAL REQUIREMENTS:

Please submit three letters of reference when applying to this position. You may upload these directly to your application or have them sent to ORNLRecruiting@ornl.gov (For postdocs, use Postdocrecruitment@ornl.gov) with 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

• Develop and apply physics-informed AI surrogate models for scientific simulations to accelerate the research in fusion energy systems.
• Advance knowledge of key AI methods such as deep learning, operator learning, and Bayesian optimization, and apply it to develop next-generation surrogate models.
• Coordinate and conduct research and experiments related to the performance and reliability of AI surrogate models.
• Develop mechanistic understanding and models for relations among simulation parameters, AI models, and predictive performance.
• Communicate and coordinate experimental results with other domain experts to facilitate collaborations.
• Present and report research results and publish scientific results in peer-reviewed journals in a timely manner.
• Ensure compliance with environment, safety, health, and quality program requirements.
• Maintain strong dedication to the implementation and perpetuation of values and ethics.