Engineering at Lawrence Livermore National Laboratory is a multidisciplinary enterprise that relies on collaborations, internal and external to LLNL, to enable innovative technological breakthroughs in fields such as materials engineering and manufacturing, bio-engineering, data analytics and machine learning, sensing, and autonomous systems to impact National Security needs and applications.
Many of our leading-edge concepts and innovations have been translated by R&D firms into commercial products that improve everyday life in novel and practical ways.
RESEARCH AREAS
- Applied Electromagnetics
- Bioengineering
- Computational Engineering
- Materials Engineering
and Manufacturing - Power and Energy Systems
- Ultrafast Optics and Photonics
Pulsed power, plasma physics, and other electromagnetic technologies sense, measure, and create extreme environments.
Biology and engineering intersect through nano-scale implantable devices, biocompatible materials, and electro-bio interfaces.
Modeling and simulating complex physical systems, processing unique signals, and applying computational power to autonomous systems.
Developing new materials, designs, and processes for next-generation manufacturing.
Addressing the need for new robust, sustainable, and secure power technologies.
Inventing optical systems for the world's most powerful lasers, ultrafast events, and national security.
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more on careersCOLLAB
more on collabWe work with industrial, university, and agency partners to transform discoveries into applications for national security and the markeplace. Got ideas? Need labs or expertise?
NEWS & EVENTS
LLNL Researchers and UC Berkeley Collaborators Featured on CBS News Report
March 31, 2019
New role for the Warm-Electron-Beam Ion Trap: NSED technicians Ed Magee, Angelique Mangoba, Harrison Flores-Alimboyoguen, and David Layne, with engineering support from Jeff Klingmann, put WEBIT on wheels and tested and readied it for departure.
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Jim Candy Writes New Textbook
2019
Jim Candy has announced the release of a new graduate text, Model-Based Processing: An Applied Subspace Identification Approach, John Wiley & Sons, 2019.
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