Accelerating scientific discovery through advanced machine learning models
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Center for Applied Scientific Computing

High-Energy-Density Science

CASC is looking for creative team members at all stages of their careers. We invite you to browse the information below and apply to our open positions. Please contact us with any questions.

High-energy-density (HED) science lets us understand the universe and new states of matter, and may be the key to virtually unlimited clean energy. ML researchers at CASC are combining some of the most sophisticated computational models, largest supercomputers, and leading-edge experimental data to create a unified framework of HED science.

We leverage the latest developments in representation learning, multimodal forward and inverse modeling, and human interpretable analysis. Join us in the quest for new discoveries in astrophysics, fusion, plasma physics, dynamic materials, and more. Learn more about an open postdoc research position on a HED science project.

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Topological Data Analysis

We evaluate data-driven models in scientific applications such as HED physics and computational biology.

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Parallelizing Training of DNNs

A novel tournament method trains traditional and generative adversarial networks for inertial confinement fusion.

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DL-Based Surrogate Models

Surrogate models supported by neural networks could lead to new insights in complicated physics problems.