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Postdoctoral Fellow - HPC Scientific Workflows (NESAP @ NERSC)
Postdoctoral Fellow - HPC Scientific Workflows (NESAP @ NERSC)Lawrence Berkeley Lab • Berkeley, CA, United States
Postdoctoral Fellow - HPC Scientific Workflows (NESAP @ NERSC)

Postdoctoral Fellow - HPC Scientific Workflows (NESAP @ NERSC)

Lawrence Berkeley Lab • Berkeley, CA, United States
4 days ago
Job type
  • Full-time
Job description

The National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab seeks a highly motivated Postdoctoral Fellow - HPC Scientific Workflows (NESAP @ NERSC) postdoctoral fellow to join the Workflow Readiness team as part of NERSC's Exascale Science Acceleration Program (NESAP). You'll work with NERSC staff, domain scientists, and engineers from industry partners to prepare key scientific workflows for the upcoming Doudna supercomputer across all program areas funded by the Department of Energy Office of Science. Doudna will deliver more than 10x the performance of Perlmutter and connect to DOE facilities for near-real-time data analysis. Your work will help 12,000+ users run faster, more reliable science.

What You Will Do :

Contribute to one or more NESAP 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.

Publish and present results at peer-reviewed venues and conferences.

Example project areas

Performance analysis and optimization of end-to-end scientific workflows, including those originating at DOE facilities.

Operating persistent or ephemeral services supporting workflows, such as databases, workflow engines, cloud-native frameworks, AI inference front ends, and REST APIs.

Coordinating dynamic service deployments and specifying storage QoS through new batch scheduler and REST API integrations.

Building customized, containerized software environments for development, CI / CD, and external sharing.

Using tools and templates for distributed AI training, agentic AI with modeling and simulation, and end-to-end workflow monitoring, profiling, and optimization.

Working with quantum simulation tools, including NVIDIA CUDA-Q, to enable scalable quantum algorithm development and quantum-HPC codesign.

What is Required :

PhD in Computer Science, Computational Science, Applied Mathematics, or a related field awarded within the last five years.

Proficiency in at least one of Python, C++, Fortran, or Julia.

Ability to work effectively in an interdisciplinary team.

Demonstrated written and oral communication of candidate-led results

Desired Qualifications :

Git and modern software practices such as unit testing, CI / CD, and collaborative development.

Experience with HPC environments and batch schedulers like Slurm.

REST API development or integration.

Workflow orchestration tools.

Data management with catalogs and transfer tools.

Container technologies such as Docker, Podman, Shifter, or Apptainer.

Kubernetes and cloud platforms.

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.

Want to learn more about working at Berkeley Lab? Please visit : careers.lbl.gov

Equal Employment Opportunity Employer : The foundation of Berkeley Lab is our Stewardship Values : Team Science, Service, Trust, Innovation, and Respect; and we strive to build community with these shared values and commitments. Berkeley Lab is an Equal Opportunity Employer. We heartily welcome applications from all who could contribute to the Lab's mission of leading scientific discovery, excellence, and professionalism. In support of our rich global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories under State and Federal law.

Berkeley Lab is a University of California employer. It is the policy of the University of California to undertake affirmative action and anti-discrimination efforts, consistent with its obligations as a Federal and State contractor.

Misconduct Disclosure Requirement : As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct, are currently being investigated for misconduct, left a position during an investigation for alleged misconduct, or have filed an appeal with a previous employer.

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