The U.S. Department of Energy (DOE) announced $13.7 million in funding for nine research projects that will advance the state of the art in computer science and applied mathematics.
The U.S. Department of Energy (DOE) announced $15.1 million for three collaborative research projects, at five universities, to advance the development of a flexible multi-tiered data and computational infrastructure to support a diverse collection of on-demand scientific data processing tasks and computationally intensive simulations.
The U.S. Department of Energy (DOE) today announced more than $13 million for five advanced-computing projects across nine states, including $4.4 million to U.S. universities.
The U.S. Department of Energy (DOE) today announced $28 million in funding for five research projects to develop software that will fully unleash the potential of DOE supercomputers to make new leaps in fields such as quantum information science and chemical reactions for clean energy applications.
Today, the U.S. Department of Energy (DOE) announced $28.9 million in funding for nine research projects to advance the development of sophisticated software for the chemical sciences.
The research described in the winning paper is focused on using a high-performance, iterative reconstruction system for noninvasive imaging at synchrotron facilities.
Researchers nationwide are building the software and applications that will run on some the world’s fastest supercomputers. Among them are members of DOE’s Exascale Computing Project who recently published a paper highlighting their progress so far.
Researchers at the McKelvey School of Engineering at Washington University in St. Louis have developed a new algorithm for solving a common class of problem — known as linear inverse problems — by breaking them down into smaller tasks, each of which can be solved in parallel on standard computers.
Students attending the last 2020 Office of Science Summer Internship Virtual Lecture Series seminar learned about how national laboratories are coming together to fight COVID-19.
Computer scientists, software developers and system administrators are coming together under one roof in the newly established Computational Sciences and Technology Division at the Department of Energy’s Thomas Jefferson National Accelerator Facility. Amber Boehnlein, Jefferson Lab’s chief information officer, has been promoted to associate director for computational sciences and technology, heading up the new division.
As part of the Department of Energy’s Office of Science Graduate Student Research (SCGSR) Program, 62 graduate students were chosen to conduct thesis research across the national laboratory complex, including 12 students at Argonne.
Argonne National Laboratory’s Illinois Mathematics and Science Academy (IMSA) High School Internship Program has this year’s exceptionally bright high school students working on the Deep Underground Neutrino Experiment (DUNE)’s world-changing research.
The U.S. Department of Energy’s Argonne National Laboratory Educational Programs and Outreach hosted the 2020 Illinois Regional Science Bowl Competition, where 15 different schools competed in trivia across a wide range of STEM topics.
On Monday, January 13, engineering students from the University of Toledo’s Roy and Marcia Armes Engineering Leaderships Institute (ELI) visited Argonne National Laboratory to prepare themselves for the leadership challenges facing engineers.
The U.S. Department of Energy’s Argonne National Laboratory’s Educational Programs and Outreach department hosted Computer Science for All — Coding and Beyond, in December as a part of the Argonne National Laboratory, Chicago initiative.
An ORNL team used the Titan supercomputer to model every building serviced by the Electric Power Board of Chattanooga—all 178,368 of them—and discovered that EPB could potentially save $11–$35 million per year by adjusting electricity usage during peak critical times.
The Science How do you determine the measurable “things” that describe the nature of our universe? To answer that question, researchers used CosmoFlow, a deep learning technique, running on a National Energy Research Scientific Computing Center supercomputer. They analyzed large,…