Two students, Ammar Haydari and Nikhil Ravi, worked with Scientific Data Division's Sean Peisert on mobility data and electrical grid data privacy projects.
Neuroscience Simulations Shed Light on Human Brain Recordings
Full-scale, biophysically accurate simulations run at NERSC used machine learning and observational data to show which neurons generate the recorded signals.
Data Management Platform Strengthens Wildland Fire Science
A new project aims to support research related to wildland fires with a searchable collaborative data repository featuring advanced search capabilities and uniform standards
Breakthrough in Quantum Universal Gate Sets
Berkeley Lab's Advanced Quantum Testbed team demonstrates a three-qubit native quantum gate with high fidelity
Advancing Battery Design with Deep Learning
A team of researchers from Berkeley Lab and UC Irvine has developed deep-learning algorithms designed to automate the quality control and assessment of new battery designs for electric cars.
Cutting Through the Noise
Berkeley Lab error mitigation approach helps quantum computers level up
Two teams led by Berkeley Lab scientists are among the finalists for the prestigious Gordon Bell Prize for two separate projects that could transform a wide range of research efforts, from decoding DNA faster to building better particle accelerators.
Following up on their late June user meeting, Lawrence Berkeley National Lab’s (Berkeley Lab’s) Neurodata Without Borders (NWB) team is co-hosting several events geared toward training participants to generate new insights from existing open neurophysiology data through secondary analysis.
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.
Researchers at the Advanced Quantum Testbed (AQT) at Berkeley Lab, in partnership with the startup Super.tech (acquired by ColdQuanta), demonstrated how a smart compiler specifically tailored for superconducting hardware can optimize circuits and networks and execute less error-prone quantum algorithms such as Quantum Approximate Optimization Algorithm (QAOA).
After working at Berkeley Lab as a Sustainable Research Pathways participant, Sofia Gomez realized she could have a career in research.