OVERVIEW:

Oak Ridge National Laboratory is the largest US Department of Energy science and energy Laboratory, conducting basic and applied research to deliver transformative solutions to compelling problems in energy and security.
The Discrete Algorithms Group at Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral researcher for a two-year position specializing in sparse algorithms. The successful candidate will contribute to advancing secure, trustworthy, and efficient AI solutions for scientific applications. Key responsibilities include developing state-of-the-art sparse algorithm in matrices, tensor and networks for large-scale numerical, scientific and AI models and disseminating findings through publications and presentations in top-tier peer-reviewed journals and conferences. This role provides a unique opportunity to work with the world’s first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric grid analytics, and scientific imaging.
The successful candidate will design and implement sparse algorithms for large-scale scientific and numerical computations. This role offers an exceptional opportunity to pursue an ambitious research agenda that will drastically advance the state-of-the-art in sparse computations both as a unified topic and within three broad pillars: sparse and structured matrix computations, sparse tensor problems, and sparse network problems, as well as their interconnections. This position offers a unique opportunity to make significant theoretical and applied contributions to sparse computations, helping to advance sparse AI and numerical systems on a global scale.

BASIC QUALIFICATIONS:

• A PhD in Computer Science, Applied Mathematics, Computational Science, or related discipline.
• Demonstrated hands-on experience and understanding of developing and applying HPC algorithms to sparse numerical, scientific and ML models.
• Demonstrated research experience with AI and ML techniques.

PREFERRED QUALIFICATIONS:

• Knowledge of HPC matrix, tensor and graph algorithms.
• Knowledge of GPU CUDA and HIP programming
• Knowledge on distributed algorithms using MPI and other frameworks such as NCCL.
• Knowledge of high-performance computing and its applications.

• Designing novel sparse algorithms for large-scale numerical, scientific and AI models.
• Developing mathematical analysis to bound the trade-off between performance, energy efficiency and time especially in the context of large AI models.
• Contribute to the research and development of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments.