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NERSC Exascale Science Applications Postdoctoral Fellow for Data (NESAP)

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POST DATE 8/11/2016
END DATE 1/12/2017

Lawrence Berkeley National Laboratory Berkeley, CA

Lawrence Berkeley National Laboratory
Job Classification
Full Time
Company Ref #
Berkeley, CA
Mid-Career (2 - 15 years)
Job Type
Doctoral Degree
AJE Ref #


Berkeley Lab (LBNL) addresses the worlds most urgent scientific challenges by advancing sustainable energy, protecting human health, creating new materials, and revealing the origin and fate of the universe. Founded in 1931, Berkeley Labs scientific expertise has been recognized with 13 Nobel prizes. The University of California manages Berkeley Lab for the U.S. Department of Energys Office of Science.

Summary: We are looking for highly motivated postdocs to join the NERSC ExaScale Application Readiness Program (NESAP), funded by the US Department of Energy Office of Science. These postdocs will participate in NESAP for Data, collaborating with scientific teams to enable the solution of high-impact data-intensive experimental/observational science problems that cannot be solved in any other way.

The Challenge: To enable advanced data science and knowledge discovery at scale on energy-efficient supercomputers. The growing scientific data deluge is increasing the demand for compute capacity. In order to meet this demand, NERSC has deployed Cori, a 30 petaflop Cray XC40 supercomputer. Cori was designed for large-scale simulations, modeling, and data analysis. The Cori system is based in part on the new Intel Knights Landing (KNL) manycore energy efficient processor. It is the first high-performance computing resource to incorporate features like the Burst Buffer, a layer of non-volatile storage, especially useful for big data workloads from scientific experiments.

NESAP for Data is an extension of the NESAP program explicitly targeting data-intensive science applications that rely on processing and analysis of massive datasets acquired from experimental and observational sources (e.g. telescopes, microscopes, genome sequencers, light sources, particle physics detectors, etc). The objective of this program is to enable such applications to take full advantage of the KNL chipset on Cori.

Many current algorithms for data analysis used in experimental science contexts are not optimized for many-core architectures. In this project, we will employ the latest advances in computer science to develop highly scalable, distributed parallel algorithms to overcome these limitations.

Fellows will be working in multidisciplinary teams composed of computer, computational, and domain scientists that will transition codes to the Cori system and produce mission-relevant science that truly pushes the limits of high-end computing. You will carry out code transition efforts in collaboration with project PI and team members and with NERSC and vendor staff.

Specific Responsibilities:


Work with NERSC staff and code teams to transition and optimize data-intensive science codes to the KNL architecture, conduct profiling and scaling studies as well as vectorization and memory bandwidth analyses for these codes, address issues specific to experimental analysis codes within the big data ecosystem at NERSC.

Disseminate results of research activities through refereed publications and conference presentations. Ensure that new methods are suitably documented for the wider experimental science community, NERSC staff, vendors, and other NERSC users.

May involve travel to sites at other labs and universities.

Participation in postdoctoral career and science enrichment activities within the Berkeley Lab Computing Sciences Area will also be encouraged.

Required Qualifications:


Ph.D. in Computer Science, Computational Science, Applied Mathematics, or a science domain area with a strong computationally-oriented research focus.

Research experience and knowledge in computing and/or code development ideally for experimental science or HPC.

Demonstrated strong communication and interpersonal skills.

Strong skills in applied mathematics, algorithm design, or scientific computing.

Ability to work productively both independently and as part of an interdisciplinary team balancing divergent objectives involving research and code development.

Additional Desired Qualifications:


Experience with the development and performance optimization of scientific software in an HPC context.

Experience with many-core and parallel computer architectures, threading and vectorization.

Experience in the development and application of modern distributed parallel computing environments such as Spark, and experience with modern high-level programming languages like Python, Julia, or Java.

Strong publication record or contributions to open source software projects

The posting shall remain open until the position is filled.



This is a full time 1 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 paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree.


Full-time, M-F, exempt (monthly paid) from overtime pay.

This position is represented by a union for collective bargaining purposes.

Salary will be predetermined based on postdoctoral step rates.

Work will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.

Equal Employment Opportunity: Berkeley Lab is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. Berkeley Lab is in compliance with the Pay Transparency Nondiscrimination Provision under 41 CFR 60-1.4. Click here to view the poster and supplement: "Equal Employment Opportunity is the Law.