Machine Learning Postdoctoral Fellow

Machine Learning Postdoctoral Fellow - 94786
Organization:  NE-NERSC

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.

What You Will Do:

• Working with domain experts and NERSC staff, develop, adapt, and optimize state-of-the-art ML/DL models to solve scientific problems on HPC systems.
• Disseminate results of research activities through refereed publications, reports, and conference presentations. Ensure that new methods are documented for the broader community, NERSC staff, vendors, and NERSC users.
• Participation in postdoctoral career and science enrichment activities within the Berkeley Lab Computing Sciences Area is encouraged.

What is Required:

• Ph.D. in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics, or another numerical science domain area.
• Research experience and knowledge in computing and/or code development for experimental science or HPC.
• Experience in building and training ML/DL models.
• Experience with machine learning/deep learning frameworks such as TensorFlow, PyTorch, scikit-learn.
• Demonstrably effective communication and interpersonal skills.
• Ability to work productively both independently and as part of an interdisciplinary team, balancing objectives involving research and code development.

Desired Qualifications:

• Publication record or contributions to open source software projects commensurate with years of experience.
• Experience or interest in distributed training of complex deep learning models on large scientific datasets.
• Experience in keeping abreast with new deep learning innovations in training algorithms and neural
• Experience with the development and performance optimization of scientific software in the HPC context.

Notes:

• This is a full-time, 2 years, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. You must have less than 4 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree.
• There are multiple openings for this position.
• This position is represented by a union for collective bargaining purposes.
• Salary will be predetermined based on postdoctoral step rates.
• This position may be subject to a background check. 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.
• Work will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.

How To Apply
Apply directly online and follow the on-line instructions to complete the application process.

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