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The Oak Ridge Leadership Computing Facility’s Matt Sieger has been named the project director for the OLCF-6 effort. This next OLCF undertaking will plan and build a world-class successor to the OLCF’s still-new exascale system, Frontier.
Juan M. Restrepo, a distinguished researcher and head of the Mathematics in Computation section at the Department of Energy’s Oak Ridge National Laboratory, has been named vice-chair of the Society for Industrial and Applied Mathematics' new Activity Group on Equity, Diversity and Inclusion. SIAM is one of the world’s premier societies for mathematics and computational sciences.
With the world’s first exascale supercomputing system now open to full user operations, research teams are harnessing Frontier’s power and speed to tackle some of the most challenging problems in modern science.
In mid-April, students from Webb School of Knoxville taking an advanced science class that focuses on quantum computing topics visited the Department of Energy’s Oak Ridge National Laboratory to tour its world-class facilities and discover the many career paths available in the ever-growing field of quantum information science.
Researchers used Oak Ridge National Laboratory’s Quantum Computing User Program to perform the first independent comparison test of leading quantum computers.
Paul Abston, leader of the HPC Infrastructure Operations Group of the National Center for Computational Sciences and manager of the Oak Ridge Leadership Computing Facility’s data center, has been named Data Center Manager of the Year for 2023.
Computing pioneer Jack Dongarra has been elected to the National Academy of Sciences in recognition of his distinguished and continuing achievements in original research.
A trio of new and improved cosmological simulation codes was unveiled in a series of presentations at the annual April Meeting of the American Physical Society in Minneapolis.
A study led by Oak Ridge National Laboratory researchers identifies a new potential application in quantum computing that could be part of the next computational revolution.
Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.