A crystal structure predicted by the GNoME AI. It contains barium (blue), niobium (white) and oxygen (green). An artificial intelligence created by Google DeepMind may help revolutionise materials science, providing new ways to make better batteries, solar panels, computer chips and many more vital technologies. "Anytime somebody wants to improve their technology, it inevitably includes improving the materials," says Ekin Dogus Cubuk at DeepMind. "We just wanted them to have more options."

Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.

A new machine learning tool aims to help scientists investigate why some people develop long COVID, a series of debilitating, chronic symptoms that last months to years after the initial COVID-19 infection. Developed by a team of researchers from institutions across the country, led by Justin Reese of Berkeley Lab and Peter Robinson of Jackson Lab, the software analyzes entries in electronic health records (EHRs) to find symptoms in common between people who have been diagnosed with long COVID and to define subtypes of the condition. The research, which is described in a new paper in eBioMedicine, also identified strong correlations between different long COVID subtypes and pre-existing conditions such as diabetes and hypertension. According to Reese, a computer research scientist in Berkeley Lab's Biosciences Area, this research will help improve our understanding of how and why some individuals develop long COVID symptoms and may enable more effective treatments by helping clinicians develop tailored therapies for each group. For example, the best treatment for patients experiencing nausea and abdominal pain might be quite different from a treatment for those suffering from persistent cough and other lung symptoms.

Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.

"Berkeley Lab is unique because its machine learning expertise is reasonably well established, and its tradition of team science means that we can work with researchers to apply these methods to scientific problems." "Although much of the time and effort spent in the software maintenance is not reflected in our research publication list, it is more than rewarding to see the wide use of this software in both the high-end scientific world and the commercial world." "I think one of the things Berkeley Lab does well is allow people to make collaborations that advance science much more efficiently." Berkeley Lab's research into machine learning builds on its foundational work in mathematics to develop methods that are consistent with physical laws, robust in the presence of noisy or biased data, and capable of being interpreted and explained in scientifically meaningful ways. Berkeley Lab Research Scientist Mariam Kiran uses deep reinforcement learning and innovative multi-objective optimization techniques to train network controllers to predict network traffic and improve traffic engineering.

Want to learn more about Berkeley Lab's Culture, Benefits and answers to FAQs? Please visit: https://recruiting.lbl.gov/

Lawrence Berkeley National Lab is hiring for Full Time 2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences - San Francisco, CA - a Mid-level AI/ML/Data Science role offering benefits such as Career development, Competitive pay, Equity, Relocation support

Scientists have developed a new machine-learning platform that makes the algorithms that control particle beams and lasers smarter than ever before. Their work could help lead to the development of new and improved particle accelerators that will help scientists unlock the secrets of the subatomic world. Daniele Filippetto and colleagues at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) developed the setup to automatically compensate for real-time changes to accelerator beams and other components, such as magnets. Their machine learning approach is also better than contemporary beam control systems at both understanding why things fail, and then using physics to formulate a response. A paper describing the research was published late last year in Nature Scientific Reports.

You must have less than 3 years of paid postdoctoral experience.

Computational Research Division (https://crd.lbl.gov/) has an opening for a AI Algorithms Postdoctoral Fellow Postdoc to join the team. In this exciting role, you will join Berkeley Lab's Computer Languages and Systems Software (CLaSS) group, a world-leading team researching and developing programming models and software for parallel and quantum computing. CLaSS research focuses on the design and development of parallel programming languages, compilers, networking middleware, runtime libraries, and quantum synthesis tools. Open-source software produced by CLaSS and its collaborators include the GASNet-EX exascale communication library, the Berkeley UPC compiler, the UPC template library, BQSKit quantum synthesis toolkit, and the OpenCoarrays parallel runtime library. CLaSS researchers collaborate with computational scientists across application domains ranging from large-scale genome assembly to materials modeling and climate simulation.