Numerical Data Compression Postdoctoral Researcher

Requirements

• Experience with high-performance computing, GPU programming, parallel programming, and/or related methods including running numerical simulations involving complex workflows.
• Experience with (multi)linear algebra, including matrix and tensor decompositions.
• Experience working with large data sets and developing scalable solutions based on distributed-memory and/or out-of-core algorithms.
• Expertise in developing software prototypes using modern languages, libraries, and tools such as C/C++/CUDA/Python, Eigen/cuSOLVER/NumPy/PyTorch, git/CMake, etc.
• Familiarity with numerical compression methods.
• Familiarity with the basic principles behind machine learning.
• Skill set at the intersection of computer science and applied mathematics.
• Demonstrated technical leadership in fields related to computer science and applied mathematics, such as mentorship or team management.
• Experience with or interest in scientific applications such as fusion, earth system science, cosmology, seismology, materials science, medicine, etc.
• Pay Range
• $143,328 Annually
• All your information will be kept confidential according to EEO guidelines.
• Position Information
• This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire date.
• Why Lawrence Livermore National Laboratory?
• Included in 2026 Best Places to Work by Glassdoor!
• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
• Our values - visit https://www.llnl.gov/inclusion/our-values
• Security Clearance
• None required. However, if your assignment is longer than 179 days cumulatively wi

Benefits

Additional Information

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. Ph.D. in Computer Science, Mathematics, or a related field. Expertise in one or more of the following areas: data compression/reduction, information theory, (multi)linear algebra, or numerical analysis. Experience developing, implementing, and applying advanced algorithms to solve large-scale numerical or combinatorial problems. Experience with scientific programming in C/C++, CUDA/HIP/SYCL/OpenMP, Python, or similar, as evidenced through software artifacts. Demonstrated research productivity, as documented by publications, reports, presentations, and/or open-source software in relevant venues (DCC, TIT, TIP, SISC, SC, ISC, IPDPS, TVCG, VIS, etc.). Proficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.

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