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Lawrence Berkeley National Lab’s (LBNL) NERSC Division has an opening for a NESAP Machine Learning Postdoctoral Fellow to join the team.

In this exciting role, 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 and future NERSC-10 system and produce mission-relevant science that pushes the limits of HPC. This position 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 machine learning, data science, HPC, and scientific computing both in industry and at national labs.

What You Will Do:

• Work with domain experts and NERSC staff to develop, adapt, and optimize state-of-the-art AI 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.
• Participate 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 AI models.
• Experience with machine learning/deep learning frameworks such as TensorFlow and PyTorch.
• 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 network architectures.
• 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 3 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree.
• This position is represented by a union for collective bargaining purposes.
• The monthly salary range for this position is $8,321-$9,646 and is expected to start at $8,321 or above. Postdoctoral positions are paid on a step schedule per union contract and salaries will be predetermined based on postdoctoral step rates. Each step represents one full year of completed post-Ph.D. postdoctoral experience.
• This position is 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.
• This position requires substantial on-site presence, but is eligible for a flexible work mode, and hybrid schedules may be considered. Hybrid work is a combination of performing work on-site at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA and some telework. Individuals working a hybrid schedule must reside within 150 miles of Berkeley Lab. Work schedules are dependent on business needs. In rare cases, full-time telework or remote work modes may be considered.