DOE CSGF (Krell) - MCS - Rovera, Giovanni - 9.1.26.

RemoteRemotePart TimePosted 12 days agoVisa sponsorship availableHidden Gem · Himalayas

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Required skills

error-bounded lossy compression
large-scale scientific data
data distortion
spatial correlations
temporal correlations
prediction models
scientific datasets
state-of-the-art
data-driven SINDy data predictor
compression ratios

Nice to have

AI governance

Role summary

This role involves investigating the use of Sparse Identification of Nonlinear Dynamics (SINDy) to improve error-bounded lossy compression for large-scale scientific data. The objective is to reduce storage and I/O costs while controlling data distortion. The work will evaluate the SINDy approach on scientific datasets, compare it with existing methods, and demonstrate its potential for improving compression ratios and data quality. The position is part-time for a visiting student graduate, requiring enrollment or recent graduation from an accredited institution, and must be conducted within the United States. Candidates will be asked to disclose participation in Foreign Government Sponsored or Affiliated Activities.

Error-bounded lossy compression has become an important technique for reducing the storage and I/O costs of large-scale scientific data while providing strict control over data distortion. Existing compressors typically exploit spatial or temporal correlations through predefined prediction models, which may not effectively capture the complex underlying dynamics of scientific datasets. In this work, we plan to investigate the use of Sparse Identification of Nonlinear Dynamics (SINDy) to improve error-bounded lossy compression. We wukk evaluate the approach on representative scientific datasets and compare it with state-of-the-art error-bounded lossy compressors. Our results will demonstrate the potential of incorporating data-driven SINDy data predictor into scientific data compression and provide insights into when SINDy-based prediction can improve compression ratios and downstream data quality.

Education and Experience Requirements

The entirety of the appointment must be conducted within the United States.
Must be 18 years or older at the time the appointment begins.

• Applicants must be:

o Currently enrolled in undergraduate or graduate studies at an accredited institution.

o Graduated from an accredited institution within the past 3 months; or

o Actively enrolled in a graduate program at an accredited institution.

Job Family

Visiting Student Graduate

Job Profile

Visiting Student - Graduate

Worker Type

Contingent Worker

Time Type

Part time

Scheduled Weekly Hours

EEO Information

As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.

Originally posted on Himalayas

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