Job Description

Overview

The National Energy Research Scientific Computing Center ( NERSC ) at Berkeley Lab seeks a highly motivated Postdoctoral Researcher Scientific Machine Learning (NESAP) to join the Workflow Readiness team as part of NERSCs Science Acceleration Program ( NESAP ). You will join a multidisciplinary team building AI-driven scientific workflows for the upcoming Doudna supercomputer. Doudna will deliver over 10x the performance of Perlmutter and connect directly to DOE experimental and observational facilities so research teams can stream and analyze data in near real time. You will collaborate with NERSC staff, domain scientists, and partners at NVIDIA and Dell to prepare high-impact workflows for 12,000+ NERSC users.

What You Will Do

• Contribute to one or more NESAP AI-based scientific workflows targeting NERSC HPC resources, edge resources, and the DOE ESnet network.
• Develop and apply advanced workflow capabilities to improve performance, portability, and productivity of scientific software.
• Collaborate with computational and domain scientists to integrate state-of-the-art AI with simulation and data analysis, including modern agentic approaches.
• Publish and present results in peer-reviewed venues.

Examples Of NESAP Project Themes

• Development, performance analysis and optimization of end-to-end science workflows, including those originating at DOE facilities.
• Deployment of capabilities such as AI training and inference at scale, and tight AI-simulation coupling.

What Is Required

• PhD in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics, or a related numerical field.
• Programming experience in one or more of: Python, C++, Fortran, Julia.
• Hands-on experience building and training AI models with frameworks such as TensorFlow or PyTorch.
• Ability to succeed in an interdisciplinary team and communicate results clearly in writing and presentations.

Desired Qualifications

• Knowledge of GPU architecture and GPU programming.
• Interest or experience in distributed training on large scientific datasets and staying current with new training methods and architectures.
• Experience with performance and profiling tools such as Perftools, NVIDIA Nsight, AMD uProf, or Omniperf.
• Debugging experience with distributed-memory parallel applications.
• Experience with containers (Docker, Podman, Shifter or similar) and modern software practices such as Git, unit testing, CI/CD, and collaborative development.
• Publications in ML-for-Science, HPC, or systems venues (SC/ISC, PPoPP, IPDPS, MLSys, NeurIPS workshops).

Preferred Application Materials

• Resume/CV
• Cover Letter

Notes

• This is a full-time, 2 year, 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 $8,570 - $9,935 and is expected to start at $8,570 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. A REAL ID or other acceptable form of identification is required to access Berkeley Lab sites.

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