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Simulations Run at NERSC Target ‘Forever Chemicals’ in Soils

MSU researchers use molecular dynamics to reveal new insights into PFAS behavior.

Neuroscience Simulation Visualization rotator crop

Neuroscience Simulations Shed Light on Human Brain Recordings

Full-scale simulations run at NERSC used machine learning and observational data to show which neurons generate the recorded signals.

Machine Learning Fuels Understanding of Materials Science

Researchers from Argonne utilized resources at NERSC to solve tough search problems and explore important questions about materials science.

AMRex Simulation Refines Mass of Elusive Axion Particle

The simulation, run at NERSC, is one of the largest ever performed in the pursuit of understanding dark matter.

Machine Learning Enables Biomolecular Breakthrough

Computational analysis of the motion of protein nanorods closes the loop between simulations and experimental data.

Deep Learning for New Battery Design

Berkeley Lab and UC Irvine researchers have developed deep-learning algorithms to automate the quality control and assessment of new battery designs for electric cars.

X-Ray Crystallography Goes Even Tinier

Scientists at Berkeley Lab have debuted a new form of X-ray crystallography for small molecules not previously conducive to investigation with the technique.

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Berkeley Lab Computing Sciences fields two highly selective named fellowships for graduates. Both the Luis W. Alvarez Fellowship in Computing and the Admiral Grace Hopper Fellowship in Computing offer exceptional opportunities for post-doctoral research. We also offer many other fellowship opportunities. Read More »
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News

Summer Research Program Wraps with Poster Sessions

August 17, 2022: The Computing Sciences Area Summer Program wrapped up another successful year with both virtual and physical poster sessions. Read More »
NERSC Summer Student Applies Machine Learning to Particle Accelerator Design Challenge

August 16, 2022: Graduate student Mikaela Meitz spent the summer working with NERSC to address long-standing challenges in the design of particle accelerators. Read More »
Exascale Application Project Targets Carbon Capture and Storage

August 15, 2022: Carbon capture and storage technologies are promising approaches for reducing CO2 emissions, but one of the biggest challenges in deploying them is the scale-up from laboratory design to industrial scale. The MFIX-Exa software subproject of the DOE’s Exascale Computing Project is helping achieve that scaling using the AMReX software framework developed at Berkeley Lab. Read More »
NERSC Summer Student Works Toward Machine Learning For a Rainy Day

August 15, 2022: As part of the 2022 Berkeley Lab Summer Student program, UC Berkeley PhD student James Duncan works to improve the resolution of precipitation models using machine learning, aiming to produce more accurate weather prediction. Read More »