HPC Data Management Postdoctoral Researcher
Lawrence Berkeley National Lab’s (LBNL) Scientific Data Management Research Group has an immediate opening for an HPC Data Management Postdoctoral Researcher to develop and optimize parallel I/O libraries and next-generation object-based storage technologies for the upcoming exascale era and beyond.
The LBNL Scientific Data Management Research Group (SDM) performs research in scientific data management and analysis for exascale computing and multi-petabyte experimental and simulation datasets. The overarching goal of the group is to enable scientific discoveries through the design, analysis, and development of extreme-scale data management technologies that allow scientists to access their data more efficiently. Members of SDM group work closely with application scientists throughout the DOE Office of Science community (e.g., astronomy, astrophysics, climate change research, fusion research, high energy physics, nuclear science, and life sciences), with faculty and students from universities throughout the world, and with staff in the NERSC production computing facility as well as other DOE Leadership Computing Facilities. Group members have access to leading-edge computing platforms.
SDM group members have a strong history of publications in top journals and conferences and have developed software systems that are broadly used outside the group. Specific areas of research being done by SDM group include: FastBit indexing technology, Storage Resource Management (SRM), Proactive Data Containers (PDC) object storage technologies, scientific data formats such as HDF5 and ADIOS, data access libraries, climate data management, high throughput network data transfers, data analysis and mining.
What You Will Do:
- Contribute to a research team focused on developing storage systems and parallel I/O technologies that affect performance of storing and accessing by scientific applications.
- Conduct research involving parallel I/O systems and performance optimization of HPC applications’ I/O.
- Develop object storage technologies for HPC.
- Analyze and optimize I/O performance on supercomputing systems.
- Develop I/O benchmarks representative of applications at supercomputing facilities.
- Document work and results in the form of journal papers and conference proceeding papers, and present work and results at scientific meetings.
- Collaborate with other computer scientists, applied mathematicians, computational scientists to ensure the resultant technologies are applicable for their respective computational challenges and coding styles.
- Work effectively with Principal Investigators, peers, and managers at LBNL, external collaborators, sponsors, and stakeholders by conducting telecons, attending workshops, writing workshop reports and conference papers, and corresponding to program managers as needed.
What is Required:
- PhD degree in applied mathematics, computer science, physics, or related fields or equivalent combination of education and experience.
- Demonstrated expertise in the implementation and optimizations I/O and storage systems.
- Demonstrated experience in working with high-performance computing applications using C/C++, MPI, and I/O libraries, such as HDF5 and MPI-IO.
- Experience in performance modeling and applying AI analysis to understand performance analysis.
- Demonstrated experience in using distributed-memory computing platforms and AI systems.
- Knowledge of C/C++, MPI, HDF5, netCDF, and MPI-IO
- Knowledge of ML and AI software libraries.
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Notes:
- 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 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 may be performed on-site, hybrid, full-time telework, or Remote near Ohio State University. The primary location for this role is Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA. Work must be performed within the United States.
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