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DOE Computational Graduate Science Fellows

A guide for DOE Computational Science Graduate Fellowship students interested in doing a practicum at Lawrence Berkeley National Laboratory.

Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to providing meaningful practicum opportunities for Department of Energy Computational Science Graduate Fellowship (CSGF) students who want to work here.

About the Lab

Lawrence Berkeley National Laboratory (Berkeley Lab) has been a leader in science and engineering research for more than 70 years. Located on a 200-acre site in the hills above the University of California's Berkeley campus and overlooking the San Francisco Bay, Berkeley Lab is a U.S. Department of Energy (DOE) National Laboratory managed by the University of California. It's annual budget for fiscal year 2011 was $735 million and employs a staff of about 4,200, including more than a thousand students.

Berkeley Lab conducts unclassified research across a wide range of scientific disciplines with key efforts in fundamental studies of the universe, quantitative biology, nanoscience, new energy systems and environmental solutions, and the use of integrated computing as a tool for discovery. It is organized into 15 scientific divisions and hosts six DOE national user facilities, including the flagship center for unclassified supercomputing, the National Energy Research Scientific Computing Center (NERSC).

CSGF Contacts at Berkeley Lab

Dan Martin, a mathematician and member of the Applied Numerical Algorithms Group in the Computational Research Division (and a CSGF alumnus), is the Lab's scientific liaison to the CSGF program and can provide specific information about research opportunities at LBNL. Dan can be contacted at DFMartin@lbl.gov.

Jon Bashor, the communications manager for the Lab’s Computing Sciences organization, is the logistics contact for CSGF students. Jon can provide general information about the Bay Area, finding housing, getting around Berkeley and completing arrangements for a practicum. Jon can be reached at JBashor@lbl.gov or +1 510 486 5849.

CSGF students at LBNL

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Practical information for getting started

Finding housing in the Bay Area: The San Francisco Bay Area has a reputation for expensive, hard-to-find housing. This is somewhat deserved, but the situation has improved in recent years. Most CSGF students have been able to find summer housing at a price they can afford. Here are some resources for finding a place to stay.

Arranging access to the Lab on your first day: Once you have found a sponsor at the Lab and agreed on a start date, contact the human resources staff in the division in which you will be working. Your sponsor can help you obtain the contact information. The HR staff will then make the necessary arrangements for site access, badge, orientation, etc.

Getting around Berkeley and the Bay Area: The San Francisco Bay Area has an extensive public transportation network, which links to the Lab’s shuttle buses. Here are links to help you find your way around the region.


About Berkeley Lab Computing Sciences

The Lawrence Berkeley National Laboratory (Berkeley Lab) Computing Sciences organization provides the computing and networking resources and expertise critical to advancing the Department of Energy's research missions: developing new energy sources, improving energy efficiency, developing new materials and increasing our understanding of ourselves, our world and our universe. ESnet, the Energy Sciences Network, provides the high-bandwidth, reliable connections that link scientists at 40 DOE research sites to each other and to experimental facilities and supercomputing centers around the country. The National Energy Research Scientific Computing Center (NERSC) powers the discoveries of 5,500 scientists at national laboratories and universities, including those at Berkeley Lab's Computational Research Division (CRD). CRD conducts research and development in mathematical modeling and simulation, algorithm design, data storage, management and analysis, computer system architecture and high-performance software implementation.