Using machine learning in over 100 different projects, Berkeley Lab scientists have tracked atomic particles, searched for better battery materials, analyzed traffic patterns, improved crop yields, pinpointed extreme weather in exascale climate simulations, and pieced together metagenomic puzzles from billions of DNA fragments. » Read More
Neurodata Without Borders: Neurophysiology Consortium rolls out v2.0 of its software ecosystem for neuroscientists. » Read More
Nearly 30 faculty attended a workshop to match professor-and-student teams from historically underrepresented or underserved institutions with lab staff for summer research opportunities. The program is operated in conjunction with the Sustainable Horizons Institute. » Read More
Berkeley Lab researchers, in collaboration with scientists from SLAC National Accelerator Laboratory and the Max Planck Institute, have demonstrated that fluctuation X-ray scattering is capable of capturing the behavior of biological systems in unprecedented detail. The novel X-ray imaging technique made possible by the CAMERA Center offers nanoscale insights into the behavior of biological molecules. » Read More
The BISICLES ice sheet model uses high performance computing resources at the National Energy Research Scientific Computing Center (NERSC) to systematically examine where the Antarctic Ice Sheet is vulnerable and the resulting potential for large contributions to sea level rise.
Two postdoctoral researchers in the Computational Research Division’s Center for Computational Sciences and Engineering won “best poster” during the recent SIAM-CSE meeting.
A new group in Berkeley Lab’s Computational Research Division (CRD) aims to tap into the lab's expertise in both computing and biosciences, crossing organizational lines to create an integrated team to develop new tools for addressing a range of scientific problems.
Berkeley Lab researchers have developed a software tool that uses supercomputing resources at the National Energy Research Scientific Computing Center to accurately simulate traffic flow throughout the San Francisco Bay Area road networks and provide estimates of the associated congestion, energy usage, and productivity loss.
