NERSC

05/13/2026

Interested in applying AI techniques to the study of cosmology?

The Fair Universe collaboration—composed of Institute for Advanced Study, Berkeley Lab, University of Washington, Université Paris-Saclay, Stanford University, University of California, Berkeley, SLAC National Accelerator Laboratory, and CHALEARN—has launched its latest competition, submitted to NeurIPS 2026 after successful competitions in 2024 and 2025. The Weak Lensing Uncertainty Challenge (Phase 2) explores out-of-distribution (OoD) detection AI techniques for Weak Gravitational Lensing Cosmology. Winners will receive a cash prize and the opportunity to present at the competition workshop submitted to NeurIPS 2026.

Phase Two has just launched and continues through October 11.

Learn more and apply: https://bit.ly/4tCylWx

U.S. Department of Energy Berkeley Lab Computing Sciences

04/22/2026

Can one AI model trained for a particular job be useful for other types of science?

That's the promise of foundation models — machine-learning models that establish a baseline of knowledge and can be used for a range of applications. They're likely to be a transformative tool in the future of scientific research.

OmniLearned, a new foundation model developed at NERSC, is an exciting development in the field: state-of-the-art in its intended discipline of particle physics, it's also proven useful in fields like cosmology and molecular physics, the first of its kind to translate in that way. It's just another way NERSC is making groundbreaking science possible!

Learn more about OmniLearned: https://bit.ly/4sRa2E7

Berkeley Lab Computing Sciences Berkeley Lab U.S. Department of Energy

04/16/2026

At NERSC, we're always looking for ways to support users of all experience levels in successfully using our systems and resources. In this video, NERSC engineer Lipi Gupta shows how to set up VSCode so you can access NERSC resources and work interactively via SSH—it's a great way to incorporate easy text editing and coding assistants and plugins into your NERSC workload. Watch the video: https://bit.ly/41XEce1

Looking for more tips, tricks, and training for working with NERSC? Check out the rest of our training videos: https://bit.ly/4aRmHzY

Berkeley Lab Computing Sciences Berkeley Lab

04/09/2026

ICYMI: Tomorrow, April 10, is the deadline to apply for the 2026 Deep Learning for Science Summer School ( )!

This five-day intensive, hosted at Berkeley Lab from July 20–24, brings together researchers and engineers to explore the latest advances in deep learning and AI. This year's program will particularly focus on foundation models, reasoning, and agentic AI for scientific discovery.

“Since we started this event in 2019, we’ve seen an explosion in the sophistication of deep learning and AI approaches used in science,” said Wahid Bhimji, Division Deputy for AI and Science at NERSC and a co-organizer of the event. “It remains our focus to bring bleeding-edge techniques from practitioners in industry, academia, and labs to the wider fundamental science community.”

Learn more and apply by Friday: bit.ly/dl4sci2026

Berkeley Lab Computing Sciences U.S. Department of Energy

Bringing researchers to the state of the art is crucial to the use of AI for science research, and this summer, that’s exactly what the National Energy Research Scientific Computing…

04/01/2026

🏆 🎉 Congratulations to NERSC Quantum Computing Advisor Katie Klymko! Katie's been recognized by the UC Womxn Rock IT 2026 campaign, an annual initiative led by the UC Womxn in Technology (WIT) Committee to celebrate the outstanding contributions of women advancing innovation, leadership, and inclusive excellence across the UC system.

Congratulations, Katie!

Learn more about this honor: https://bit.ly/4dVSi6v

Berkeley Lab Berkeley Lab Computing Sciences

03/31/2026

“We’ve seen an explosion in the sophistication of deep learning and AI approaches used in science. It remains our focus to bring bleeding-edge techniques from practitioners in industry, academia, and labs to the wider fundamental science community.” - Wahid Bhimji, Division Deputy for AI and Science at NERSC

AI shows vast potential for accelerating and deepening the scope of scientific discovery. Are you ready to put it to work?

The Deep Learning for Science Summer School ( ), hosted at Berkeley Lab from July 20–24, is a five-day intensive program assembling researchers and engineers to explore the latest advances in deep learning and AI. Built on a blend of theory, hands-on application, and opportunities for networking and collaboration, the 2026 program will focus on agentic AI and foundation models for scientific discovery. Apply by April 10.

Learn more and apply: https://bit.ly/dl4sci2026

Berkeley Lab Computing Sciences U.S. Department of Energy

Bringing researchers to the state of the art is crucial to the use of AI for science research, and this summer, that’s exactly what the National Energy Research Scientific Computing…

03/17/2026

NERSC’s upcoming Doudna supercomputer is poised to make big impacts in scientific computing. To ensure a smooth deployment of this complex system, an Early Access System, to be called Cech, is now installed at NERSC. This smaller system offers NERSC and its partner vendors Dell Technologies, NVIDIA, and VAST Data the opportunity to refine assembly, delivery, installation, and integration processes, paving the way for seamless deployment of the full-scale Doudna system in late 2026.

Learn more: https://bit.ly/4bNMivd

U.S. Department of Energy Berkeley Lab Computing Sciences Berkeley Lab

The road to NERSC’s Doudna supercomputer reached a key milestone with the delivery of the Early Access System (EAS), to be known as Cech.

03/16/2026

Attending NVIDIA ? HPC experts from NERSC are, and they'll be sharing their expertise all week. Don't miss the following sessions featuring NERSC staff doing what they do best:

Pioneering Autonomous Science: Toward a Unified Cross-Lab Workflow [S81523]
Bjoern Enders
Tuesday, Mar 17
11:00 - 11:40
Schedule link: https://ow.ly/PRUf50Yv2EE

Quantum Computing at NERSC and Lawrence Berkeley National Laboratory [S81696]
Katie Klymko
Wednesday, Mar 18
4:00 PM - 4:40 PM PDT
Schedule link: https://ow.ly/nFeJ50Yv2EI

Enhance Data Center Efficiency and Throughput by Co-Scheduling on NVIDIA CPU+GPU Systems [S81853]
Brian Austin
Thursday, Mar 19
11:00 AM - 11:40 AM PDT
Schedule link: https://ow.ly/94qL50Yv2EH

Workflow Revolution: How Doudna Drives HPC, AI, and Quantum Scientific Impact [S81634]
Rollin Thomas
Thursday, Mar 19
12:00 PM - 12:40 PM PDT
Schedule link: https://ow.ly/aMsP50Yv2EK

See the whole catalog: https://ow.ly/3tIC50Yv2EC

Berkeley Lab Computing Sciences Berkeley Lab

In this talk, we highlight how emerging quantum computing technologies are poised to impact the National Energy Research Scientific Computing Cente...

03/13/2026

NERSC Director Sudip Dosanjh recently sat down with our vendor partner, Dell Technologies, to discuss the promise of the upcoming Doudna supercomputer — not a one-of-a-kind system, but a first-of-a-kind system, setting the stage for breakthroughs across scientific computing.

Learn more: https://ow.ly/Ayk550YtxeF

Berkeley Lab Berkeley Lab Computing Sciences

03/05/2026

Thermodynamic noise hinders the efficiency of computing. But what if that noise could be used as a power source instead? Computers powered by thermal noise could dramatically reduce the energy demands of AI and other computing processes.

Using thermodynamic noise to power computers, rather than inhibit them, is the goal of the field of thermodynamic computing, and a collaboration between researchers at the Molecular Foundry and NERSC is bringing it closer to reality for both classical and quantum computers. They’ve proposed a design and training framework for a type of thermodynamic computer that mimics a neural network, which could drastically reduce the energy requirements of machine learning.

Learn more: https://bit.ly/40cPGcA

U.S. Department of Energy Berkeley Lab Computing Sciences Berkeley Lab