Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
-edge high-performance computing (HPC) that incorporate machine learning/artificial intelligence (ML/AI) techniques into visualizations, enhancing the efficiency and reliability of scientific discovery
-
, work together, and measure success. Basic Qualifications: A PhD degree in Computer Science or a related discipline. A strong background in scientific data visualization, uncertainty quantification, AI/ML
-
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
-
of conduct, and a statement by the Lab Director's office can be found here: https://www.ornl.gov/content/research-integrity Basic Qualifications: Ph.D. in mathematics, engineering, data/computational science
-
complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section of the Computer Science and Mathematics (CSM) Division. CSM delivers fundamental and applied research
-
Fellowship in mathematics and scientific computing. This prestigious postdoctoral fellowship is supported by the Applied Mathematics Research Program in the U.S. Department of Energy’s Office of Advanced
-
topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work
-
for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section
-
dimensional reduction methods and visualization tools. Effective writing and communication skills as demonstrated in publication and presentation. Demonstrated ability to work both independently and
-
mathematics and scientific computing. This prestigious postdoctoral fellowship is supported by the Applied Mathematics Research Program in the U.S. Department of Energy’s Office of Advanced Scientific Computing