Berkeley Lab addresses the world's 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 Lab's scientific expertise has been recognized with 13 Nobel prizes. The University of California manages Berkeley Lab for the U.S. Department of Energy Office of Science.
NERSC provides world-class supercomputing, high performance, scalable data systems and services to 7000 users across more than 600 projects running 800 different codes. NERSC's science impact is acknowledged by over 2000 publications per year.
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.
NESAP for Data (N4D):To answer today's most complex experimental challenges, scientists are collecting exponentially more data and analyzing it with new computationally intensive algorithms. N4D addresses data-intensive science pipelines that process massive datasets from experimental and observational science (EOS) facilities like synchrotron light sources, telescopes, microscopes, particle accelerators, or genome sequencers. The goal is seamless integration and data flow between EOS facilities and supercomputing resources to enable scalable real-time data analytics.
NESAP for Learning (N4L): Machine Learning (ML) and Deep Learning (DL) are powerful approaches to solving complicated classification, regression, and pattern recognition problems. N4L focuses on developing and implementing cutting-edge ML/DL solutions to improve scientific discovery potential on experimental or simulation data or improving HPC applications by replacing parts of the software stack or algorithms with ML/DL solutions.
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.
As a NESAP Fellow, 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.
Successful candidates will have one or more of the following responsibilities:
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.
All postdocs will have the responsibility to:
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.
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.
Demonstrably effective communication and interpersonal skills.
Experience in scientific computing, algorithms design, or applied mathematics.
Ability to work productively both independently and as part of an interdisciplinary team balancing objectives involving research and code development.
Additional 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.
NESAP for Simulation
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.
NESAP for Data
Experience with at least one high-level language (HLL) such as Python, Julia, or R and corresponding data analytics package ecosystem. Awareness of issues associated with optimizing and parallelizing HLL-based codes is a plus.
Familiarity with libraries or frameworks that enable productive data analytics, improve parallelism in general, or provide GPU acceleration for HLLs. For example: Numba, Pandas, DataFrames.jl, Dask, Spark, or mpi4py.
Interest in one or more of the following areas: Container technologies (e.g. Docker), Jupyter notebooks, complex workflows and pipelines, and/or adapting data analytics tools to an HPC environment.
NESAP for Learning
Experience with machine learning/deep learning frameworks such as TensorFlow, PyTorch, scikit-learn
Experience in building and training ML/DL models and keeping abreast with new deep learning innovations in training algorithms and neural network architectures.
Experienced or interested in distributed training of complex deep learning models on large scientific datasets
The posting shall remain open until the positions are filled.
To be considered applications must include:
A Cover Letter: Include a cover letter introducing yourself, your application, and describing your interest in the program. Please be sure to highlight which NESAP program area interests you most: Simulation, Data, or Learning.
Curriculum Vitae/Resume: Either an academic CV or a resume is acceptable. Be sure to highlight technical skills, interests, and synergistic activities relevant to the position and to NERSC.
List of Publications: A list of publications is encouraged. Links to software projects, public code repositories, and other non-standard career metrics are welcome!
3 References: Provide contact information for three professional references with whom we may communicate regarding your work and your application.
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. Clickhere to view the poster and supplement: "Equal Employment Opportunity is the Law."
Internal Number: 85862
About Lawrence Berkeley National Laboratory
In the world of science, Lawrence Berkeley National Laboratory (Berkeley Lab) is synonymous with excellence. Thirteen scientists associated with Berkeley Lab have won the Nobel Prize. Fifty-seven Lab scientists are members of the National Academy of Sciences (NAS), one of the highest honors for a scientist in the United States. Thirteen of our scientists have won the National Medal of Science, our nation's highest award for lifetime achievement in fields of scientific research. Eighteen of our engineers have been elected to the National Academy of Engineering, and three of our scientists have been elected into the Institute of Medicine. In addition, Berkeley Lab has trained thousands of university science and engineering students who are advancing technological innovations across the nation and around the world. Berkeley Lab is a member of the national laboratory system supported by the U.S. Department of Energy through its Office of Science. It is managed by the University of California (UC) and is charged with conducting unclassified research across a wide range of scientific disciplines. Located on a 200-acre site in the hills above the UC Berkeley campus that offers spectacular... views of the San Francisco Bay, Berkeley Lab employs approximately 4,200 scientists, engineers, support staff and students. Its budget for 2011 is $735 million, with an additional $101 million in funding from the American Recovery and Reinvestment Act, for a total of $836 million. A recent study estimates the Laboratory's overall economic impact through direct, indirect and induced spending on the nine counties that make up the San Francisco Bay Area to be nearly $700 million annually. The Lab was also responsible for creating 5,600 jobs locally and 12,000 nationally. The overall economic impact on the national economy is estimated at $1.6 billion a year. Technologies developed at Berkeley Lab have generated billions of dollars in revenues, and thousands of jobs. Savings as a result of Berkeley Lab developments in lighting and windows, and other energy-efficient technologies, have also been in the billions of dollars. Berkeley Lab was founded in 1931 by Ernest Orlando Lawrence, a UC Berkeley physicist who won the 1939 Nobel Prize in physics for his invention of the cyclotron, a circular particle accelerator that opened the door to high-energy physics. It was Lawrence's belief that scientific research is best done through teams of individuals with different fields of expertise, working together. His teamwork concept is a Berkeley Lab legacy that continues today.