NESAP for Simulations Postdoctoral Fellow

The Challenge: Enabling simulation of complex physical systems, advanced data analytics, and machine learning at scale on energy-efficient supercomputers. Successful candidates will join different teams to focus on one of the following frontiers:

NESAP for Simulations (N4S): Cutting-edge simulation of complex physical phenomena requires increasing amounts of computational resources due to factors such increasing model sizes, parameter space searches, and inclusion of additional physics. N4S enables simulations to make effective use of modern high-performance computing (HPC) platforms by focusing on algorithm and data structure development and implementation on new architectures such as GPUs, exposing additional parallelism and improving scalability.

To enable new discoveries through simulation, data analytics, and ML/DL, NERSC will begin deploying “Perlmutter,” a Cray supercomputer, in 2020. Perlmutter, a system optimized for science, is a heterogeneous system including future-generation AMD CPUs and next-generation NVIDIA GPUs. It also has a high-speed interconnect and an all-flash file system.

Perlmutter is NERSC’s first production GPU-based system. Many codes running at NERSC need to be adapted or optimized to run efficiently on GPUs. At the same time, solutions that put GPU performance in users’ hands need to be portable ones. NESAP is about employing cutting-edge computer science and advanced performance analysis tools to develop highly scalable, distributed parallel algorithms to meet this challenge.

In this exciting role, you will be a part of a multidisciplinary team composed of computational and domain scientists working together to transition and optimize codes to the Perlmutter system and produce mission-relevant science that pushes the limits of HPC. You will carry out code transition efforts in collaboration with a project PI and team members with the support of NERSC and vendor staff. Successful candidates are expected to collaborate with each other across NESAP program areas (N4S, N4D, N4L).

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. Take a look at what current and former NESAP postdocs are up to here.

What You Will Do:

• Work with NERSC staff and code teams to transition and optimize simulation, data analytics, or machine learning codes for the Perlmutter system in performance-portable ways.
• Conduct profiling and scaling studies as well as parallelization, memory bandwidth, and I/O analyses for these codes; identify and capitalize on NERSC’s combined HPC/data ecosystems.
• Working with domain experts, 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.
• Opportunities to travel to sites at other labs, universities, and to vendor facilities.

What is Required:

• Ph.D. in Computational Science, Data Science, Computer Science, Applied Mathematics, or a science domain area with a computationally-oriented research focus.
• Research experience and knowledge in computing and/or code development for experimental science or HPC.
• Experience in scientific computing, algorithms design, or applied mathematics. 
• 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.
• Experience with GPU and parallel/manycore computer architectures, threading, and vectorization.
• Experience with numerical linear algebra, particle methods, grid methods.
• Experience with C, C++, Fortran, MPI, threading, and data structure transformations.

Desired Qualifications:

• Experience with the development and performance optimization of scientific software in the HPC context.
• Publication record or contributions to open-source software projects commensurate with years of experience.

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.
• 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.

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