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Machine Learning for Science Campaign

September 17, 2020

As experiments produce vastly more data than ever before, machine learning methods tailored to scientific data provide powerful tools for analyzing these complex datasets. Berkeley Lab researchers have been addressing these challenges for the last several years, and in August-September 2020, we featured some of their projects and postdocs in a social media campaign. Read More »

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ESnet Summer Students Make the Most of Their Remote Internships

September 8, 2020

Like many organizations adjusting to a changing work environment amid the COVID-19 pandemic, all of ESnet’s summer students did their internships remotely this year. As their internships draw to a close, we talked with a few of our students about their experiences. Read More »


NERSC Summer Students Take Deep Learning to the Next Level

September 1, 2020

NERSC summer students this year focused on applying deep learning methods to an array of science applications, from weather and climate modeling to improving I/O scaling for GPUs. Read More »

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Berkeley Lab’s ENDURABLE: An Aggregate Data Standard for AI Modeling

August 31, 2020

DOE announced $8.5 million for projects to make artificial intelligence models and data more accessible and reusable. One of these newly funded projects is “ENDURABLE: Benchmark datasets for AI with queryable metadata,” spearheaded by Berkeley Lab’s Computational Research Division. Read More »

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New $115 Million Quantum Systems Accelerator to Pioneer Quantum Technologies for Discovery Science

August 26, 2020

The Department of Energy has awarded $115 million over five years to the Quantum Systems Accelerator (QSA), a new research center led by Berkeley Lab that will forge the technological solutions needed to harness quantum information science for discoveries that benefit the world Read More »

Karthik Kashinath

Physical Scientists Turn to Deep Learning to Improve Earth Systems Modeling

August 24, 2020

Weather, climate, and Earth systems modeling are emerging as an exciting application area for physics-informed deep learning that can more effectively identify nonlinear relationships in large datasets, extract patterns, emulate complex physical processes, and build predictive models. Read More »

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Searching for Evidence of a ‘Neutrinoless’ Particle Process

August 21, 2020

Nuclear physicists affiliated with Berkeley Lab played a leading role in analyzing data at NERSC for CUPID-Mo, a demonstration experiment that has achieved record precision for a specialized detector material. Read More »

EsmondP Ng Lenny Oliker

Berkeley Lab staff hold leadership roles in SciDAC Institutes

August 12, 2020

Over the next five years, the U.S. Department of Energy will provide $57.5 million for two Scientific Discovery through Advanced Computing Institutes. And Berkeley Lab staff will continue to hold leadership positions in both—Esmond Ng as director of FASTMath and Lenny Oliker as deputy director of RAPIDS2. Read More »

Steve Farrell

Graph Neural Networks Open New Doors in Particle Physics Research

August 11, 2020

High-performance data analysis tools are helping the HEP research community find more efficient ways to sift through, reconstruct, and analyze the increasing amounts of detector data available to them. Read More »

Sarah McGinn

Meet Sarah McGinn, CSA’s New Safety Coordinator

August 10, 2020

Sarah McGinn recently joined Berkeley Lab’s Computing Sciences Area (CSA) as the Division Safety Coordinator for CSA and IT. Read More »