Numerical Data Compression Postdoctoral Researcher

We have an opening for a Postdoctoral Research Staff Member to contribute to fundamental R&D in numerical data compression in support of projects related to AI-based surrogate modeling, scientific computing, and physical and life sciences that generate vast quantities of experimental and observational data.  This R&D will primarily focus on basic research to advance state of the art in lossy numerical data compression based on tensor decomposition methods for three- and higher-dimensional data.  Specific goals include the advancement of (1) new coding schemes, number representations, and compact parameterizations of tensorial data; (2) numerical analysis to characterize error distributions and guarantee error bounds; and (3) development of highly scalable and performant compression algorithms that exploit data parallelism on GPUs and multicore architectures.  This position will be in the Data Science & Analytics Group in the Center for Applied Scientific Computing (CASC) Division within the LLNL Computing Directorate.

In this role you will:

• Research, design, implement, and apply advanced numerical data compression and/or tensor decomposition methods (Tucker, TT, CP, etc.).
• Make independent contributions to one or more project thrusts on novel coding methods, error analysis, and performance optimization.  Document results in technical reports and peer-reviewed publications.
• Work with domain scientists to evaluate the effectiveness of lossy compression methods and their impact on accuracy, storage, and performance within scientific workflows (e.g., surrogate modeling, scientific data analysis, numerical simulation, etc.).
• Contribute to grant proposals and collaborate with others in a multidisciplinary team environment, including academic and industrial partners, to accomplish research goals.
• Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internal and external to the Laboratory.
• Perform other duties as assigned.