[patfresh]

The National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab seeks highly motivated Machine Learning Postdoctoral Fellows to join the NERSC Exascale Science Application Program (NESAP). NESAP postdocs collaborate with scientific teams to enable the solution of deep, meaningful problems across all program areas funded by the Department of Energy Office of Science.

The Challenge: Enabling machine learning at scale on energy-efficient supercomputers.

NESAP for Learning (N4L): Machine Learning (ML) and Deep Learning (DL) are powerful approaches to solving complicated classification, regression, and pattern recognition problems. N4L focuses on developing and implementing cutting-edge ML/DL solutions to improve scientific discovery potential on experimental or simulation data or improving HPC applications by replacing parts of the software stack or algorithms with ML/DL solutions.

To enable new discoveries through simulation, data analytics, and ML/DL, NERSC began deploying “Perlmutter,” a Cray supercomputer, in 2021. Perlmutter, a system optimized for science, is a heterogeneous system including current-generation AMD CPUs and NVIDIA GPUs. It also has a high-speed interconnect and an all-flash file system.

As a NESAP Fellow, you will be a part of a multidisciplinary team composed of computational and domain scientists working together to develop machine learning approaches that run on the Perlmutter system and produce mission-relevant science that pushes the limits of HPC. You will carry out these efforts in collaboration with a project PI and team members, with the support of NERSC and vendor staff.

NESAP has established a track record of enabling its postdocs to pursue careers in data science, HPC, and scientific computing both in industry and at national labs.

Read more / apply: https://ai-jobs.net/...octoral-fellow/