Whether running extreme-scale simulations on a supercomputer or applying machine learning or data analysis to massive datasets, scientists today rely on advances in and integration across applied mathematics, computer science, and computational science, as well as large-scale computing and networking facilities, to increase our understanding of ourselves, our planet, and our universe.
Berkeley Lab's Computing Sciences Area researches, develops, and deploys new tools and technologies to meet these needs and to advance research in our core capabilities of applied mathematics, computer science, and computational science. In addition to fundamental advances in our core capabilities, we impact such areas as materials science, chemistry, biology, astrophysics, climate change, combustion, fusion energy, high performance computing (HPC) systems, and network technology.
Some Computing Sciences research areas include the following:
- Developing scientific applications and software technologies for extreme-scale and energy-efficient computing
- Developing mathematical modeling for complex scientific problems
- Designing algorithms to improve the performance of scientific applications
- Researching digital and post-digital computer architectures for science
- Advancing extreme-scale scientific data management, analysis, and machine-learning
- Developing next-generation machine learning and AI approaches for science
- Advancing quantum computing technologies, software, algorithms, and applications
- Evaluating or developing new and promising HPC systems and networking technologies
- Researching methods to control and manage dynamic circuit networks
- Developing large-scale visualization and analytics technologies
- Managing scientific data in distributed environments
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