What are the responsibilities and job description for the NESAP Machine Learning Postdoctoral Fellow position at Association for the Advancement of Artificial Intelligence?
Lawrence Berkeley National Lab's ( LBNL ) NERSC Division has an opening for a NESAP Machine Learning Postdoctoral Fellow to join the team.
In this exciting role, you will be a part of a multidisciplinary team composed of computational and domain scientists working together to develop machine learning approaches that run on the Perlmutter and future NERSC-10 system and produce mission-relevant science that pushes the limits of HPC. This position 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 machine learning, data science, HPC, and scientific computing both in industry and at national labs.
What You Will Do :
Work with domain experts and NERSC staff to develop, adapt, and optimize state-of-the-art AI 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.
Participate in Postdoctoral career and science enrichment activities within the Berkeley Lab Computing Sciences Area is encouraged.
What is Required :
Ph.D. in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics, or another numerical science domain area.
Research experience and knowledge in computing and / or code development for experimental science or HPC.
Experience in building and training AI models.
Experience with machine learning / deep learning frameworks such as TensorFlow and PyTorch.
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.
Desired Qualifications :
Publication record or contributions to open source software projects commensurate with years of experience.
Experience or interest in distributed training of complex deep learning models on large scientific datasets.
Experience in keeping abreast with new deep learning innovations in training algorithms and neural network architectures.
Experience with the development and performance optimization of scientific software in the HPC context.
Notes :
This is a full-time, 2 years, 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 $8,321-$9,646 and is expected to start at $8,321 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.
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