Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction. Strong background in computational sciences, including numerical methods, high-performance computing
-
through the High Flux Isotope Reactor, the Radiochemical Engineering Development Center, ORNL’s other nuclear facilities, and an assemblage of world-leading scientists and engineers. Please visit https
-
that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance and edge computing; The design of architectures
-
for the design and analysis of computational methods that accelerate data analytics and machine learning, especially as the apply to scalable high-performance computing, cloud computing, and large interconnected
-
through the High Flux Isotope Reactor, the Radiochemical Engineering Development Center, ORNL's other nuclear facilities, and an assemblage of world-leading scientists and engineers. Please visit https
-
through the High Flux Isotope Reactor, the Radiochemical Engineering Development Center, ORNL's other nuclear facilities, and an assemblage of world-leading scientists and engineers. Please visit https
-
workflows that enable reliable extraction of magnetic interactions from neutron and complementary measurements in a high-performance computing environment. Major Duties/Responsibilities: Develop and apply
-
-edge high-performance computing (HPC) that incorporate machine learning/artificial intelligence (ML/AI) techniques into visualizations, enhancing the efficiency and reliability of scientific discovery
-
scientific computing, control theory, data science, data driven methods, discrete mathematics, graph algorithms, high-performance computing, integral equations and nonlocal models, linear and multilinear
-
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