NESAP for Learning (N4L): focuses on developing and implementing cutting-edge Machine Learning (ML) and Deep Learning (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.
NERSC provides world-class supercomputing to 7000 users with a science impact acknowledged by over 2000 publications per year. In 2020, NERSC will begin deploying a new Cray supercomputer, "Perlmutter." with next-generation AMD CPUs, NVIDIA GPUs, a novel high-speed interconnect, and an all-flash file system. The NESAP program will enable new scientific advancements using Perlmutter by working with scientists to exploit cutting-edge ML/DL techniques.
As a NESAP Fellow, you will be a part of a multidisciplinary team composed of computational and domain scientists working together to develop new AI and deep learning approaches and execute them on the Perlmutter system to produce mission-relevant science that pushes the limits of AI on HPC. You will carry out these efforts in collaboration with a project PI and team members with the support of NERSC and vendor staff. 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.
What You Will Do:
Work with NERSC staff and project teams to develop machine learning for science software for the Perlmutter system.
Working with domain experts, develop, adapt, and optimize state-of-the-art ML/DL models to solve scientific problems on HPC systems.
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.
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.
What is Required:
Ph.D. in Computational Science, Physical Sciences, Data Science, Computer Science, Applied Mathematics, or another science domain area with a computationally-oriented research focus.
Research experience and knowledge in computing and/or code development for machine learning, experimental science or HPC.
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 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.
Publication record or contributions to open source software projects commensurate with years of experience.
Experience in scientific computing, algorithms design, and/or applied mathematics in the HPC context.
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 4 years 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.
Learn About Us:
Working at Berkeley Lab has many rewards including a competitive compensation program, excellent health and welfare programs, a retirement program that is second to none, and outstanding development opportunities. To view information about the many rewards that are offered at Berkeley Lab- Click Here.
Berkeley Lab (LBNL) 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's Office of Science.
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."
Lawrence Berkeley National Laboratory encourages applications from women, minorities, veterans, and other underrepresented groups presently considering scientific research careers.
Internal Number: 90514
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.