Postdoctoral Appointee - HPC and Machine Learning

Argonne National Laboratory

Lemont, IL

Job posting number: #7084229

Posted: August 30, 2021

Application Deadline: Open Until Filled

Job Description

We are seeking self-motivated and independent postdoc researchers with strong background on Machine Learning, system design, and coding skills (C/C++). The selected candidate will work closely with the SZ compression team at Argonne to develop the cutting-edge lossy compression libraries/tools practically for the scientific community. The SZ team (http://szcompressor.org) is the leading team in the error-bounded lossy compression domain. The flagship software - SZ has been verified as one of the best error-bonded lossy compressors in the community by many domain scientists independently. Joining SZ team will have exceptional opportunities to collaborate with top-tier scientists in different domains and use cutting-edge supercomputers (Aurora, Summit, etc.).

Today's scientific applications are producing extremely large volumes of data, which are causing serious issues including storage burden, I/O bottlenecks, communication bottlenecks, and insufficient memory. Error-controlled lossy compression has been recognized as one of the most efficient solutions to resolve the big scientific data issue. Existing state-of-the-art lossy compressors, however, are all developed based on fixed/static compression models or pipeline, which cannot adapt to diverse data characteristics and sophisticated user-requirements. In this project, we will develop a scalable dynamic data reduction framework, which can optimize lossy compression for various use-cases dynamically and efficiently. The key techniques include using ML/DL to explore the diverse correlations of high-dimensional science datasets, using ML/DL to identify the best-qualified data compression model dynamically, using ML/DL to optimize the parameter configurations of various compression methods, using ML/DL to denoise datasets and/or recovering missing features for reconstructed data.

Education and Experience Requirements

The selected candidate will work closely with the SZ compression team at Argonne to develop the cutting-edge lossy compression libraries/tools practically for the scientific community. Joining the SZ team will have exceptional opportunities to collaborate with top-tier scientists in different domains and use cutting-edge supercomputers (Aurora, Summit, etc.).

Requirements:

Strong background on ML, system design, and coding skills (C/C++)
Recent, or soon-to-be-completed PhD + 0-3 years of experience
This job description documents the general nature of work but is not intended to be a comprehensive list of all activities, duties and responsibilities required of job incumbent. Consequently, job incumbent may be required to perform other duties as assigned.

Job Family

Postdoctoral Family
Job Profile

Postdoctoral Appointee
Worker Type

Long-Term (Fixed Term)
Time Type

Full time
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