Postdoctoral Researcher - HPC Workflow Performance (NESAP/NERSC) at Lawrence Berkeley National Laboratory

Berkeley, California, United States -

Full Time

Start Date

Immediate

Expiry Date

12 Jan, 26

Salary

9935.0

Posted On

14 Oct, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, C++, Fortran, Julia, High-Performance Computing, Parallel Computing, GPU Architecture, Performance Tools, Distributed Memory Applications, Containers, Git, MPI, Kokkos, OpenMP, CUDA, Scientific Libraries

Industry

Research Services

Description

The National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab seeks a highly motivatedPostdoctoral Researcher - HPC Workflow Performance (NESAP/NERSC) to join the Workflow Readiness team as part of NERSC’s Exascale Science Acceleration Program (NESAP). You will join the team to prepare scientific workflows for NERSC’s next system, the Doudna supercomputer across all program areas, which will deliver over 10x the performance of Perlmutter and connect directly to DOE experimental and observational facilities. You will help 12,000+ science users run complex, data-intensive workflows at scale and in near real time. You will work closely with NERSC staff and partners at NVIDIA and Dell to turn cutting-edge capabilities into everyday tools for the science community. What You Will Do: Contribute to one or more NESAP workflow projects (https://www.nersc.gov/what-we-do/support-for-scientists/nersc-science-acceleration-program/nesap-for-doudna) using NERSC HPC resources, edge resources, and the DOE ESNET network. Develop and apply advanced workflow capabilities that improve performance, portability, and productivity. Perform performance analysis and optimization across end-to-end workflows, including those originating at DOE facilities. Publish and present results at peer-reviewed venues and conferences. What is Required: Ph.D. in Computer Science, Computational Science, Applied Mathematics, or a related field, awarded within the last five years Programming experience in one or more of Python, C++, Fortran, or Julia Knowledge of high-performance and parallel computing Experience working in interdisciplinary teams Clear record of communicating original results in writing and presentations Desired Qualifications: Knowledge of GPU architecture and experience programming GPUs Familiarity with performance tools such as NVIDIA Nsight, Perftools Experience debugging distributed memory applications Experience with containers such as Docker, Podman, or Shifter Git and modern software practices such as unit testing and CI/CD Experience with MPI, Kokkos, OpenMP, CUDA/HIP Experience implementing or optimizing scientific libraries such as Linear Algebra Libraries, Solvers, FFTs/cuFFT, sequence alignment, and similar Experience deploying and using HPC applications on Slurm clusters 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.

Responsibilities

You will contribute to NESAP workflow projects using NERSC HPC resources and develop advanced workflow capabilities. You will perform performance analysis and optimization across end-to-end workflows and publish results at peer-reviewed venues.