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CSA's Teresa Montero to Retire After More than a Decade at the Lab
Teresa Montero, front desk administrative assistant in Berkeley Lab’s Computing Sciences Area directorate since 2010, will retire January 8. Read More »
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AI for More Efficient Flying Qubits
A Berkeley Lab team led by post-doctoral researcher Mekenna Metcalf is using artificial intelligence techniques to boost the efficiency of a key process that enables qubits to "fly" across optical network fiber. Read More »
Three CRD Researchers Elected AAAS Fellows
CRD's Bert de Jong, Esmond Ng, and Jon Douglas Owens are 2020 AAAS Fellows. Read More »
SC20 Wrap-up: From the Gordon Bell Prize to Machine Learning, Quantum Computing, and More
As they have for nearly three decades, Berkeley Lab Computing Sciences Area staff from the Computational Research Division, ESnet, and NERSC shared their expertise with the global HPC community at SC20. Read More »
DOE Announces 2021 INCITE Allocation Awards
Berkeley Lab researchers will lead or co-lead six projects, ranging from cosmology and earth systems modeling to sustainable energy technologies and materials design and discovery. Read More »
Machine Learning Software that Enhances Molecular Dynamics Modeling Wins Gordon Bell Prize
CRD researchers are co-authors on a research paper that introduces a new machine-learning-based software package for molecular dynamics modeling has won the 2020 ACM Gordon Bell Prize. Read More »
Computing Sciences’ Deb Agarwal and Kathy Yelick Receive Director’s Awards
Deb Agarwal, head of the Data Science and Technology Department in the Computational Research Division, and Kathy Yelick, former Associate Lab Director for Computing Sciences, will be presented with the Berkeley Lab Citation at 3 p.m. on Thursday, November 12. Read More »
CRD/NERSC-led Paper a Gordon Bell Finalist at SC20
This work demonstrates advances made to the BerkeleyGW code that enables large-scale, excited-state calculations to run in minutes on current HPC systems. Read More »
Machine Learning to Add Another Dimension to ESnet's Toolbox for Predicting Data Patterns
A research project called DAPHNE, led by ESnet’s Mariam Kiran, is exploring how artificial intelligence can be used to design and efficiently manage distributed network architectures to improve data transfers, guarantee high-throughput, and improve traffic engineering. Read More »