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multidisciplinary team of scientists and High Performance Computing (HPC) engineers. In the AL/ML group, we work at the forefront of HPC to push scientific boundaries, carrying out research and development in state
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(HPC). The postdoc will work closely with visualization researchers, AI scientists, and domain application teams across Argonne and the broader DOE ecosystem. The goal of this postdoctoral position is to
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the ability and motivation to develop expertise in large-scale model training and scaling on HPC systems, as well as in handling the unique characteristics of scientific data, including large-scale numerical
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: Contributes technical expertise through analysis and support for programs and projects associated with machine learning, HPC, and computational problems related to earth system science and other dynamical
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(HPC): Experience with parallel computing (MPI, OpenMP, CUDA/HIP) or running workflows on supercomputing clusters. Software Engineering: Knowledge of version control (Git), containerization (Docker
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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science, including electronic structure methods molecular dynamics, and scientific machine learning. Experience with High-Performance Computing (HPC) systems and intelligent workflows. Demonstrated
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(HPC) environments and large-scale data management. Experience with version control (e.g., Git) and collaborative software development practices. Experience with real-time analysis and instrument control
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: - Comprehensive understanding of applied computational materials science, including electronic structure methods and molecular dynamics. - Experience with High-Performance Computing (HPC) systems and intelligent
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. Experience in numerical methods and CFD development using mesh-based scientific codes. Expertise in the lattice Boltzmann method (LBM) as evidenced by their publications High performance computing (HPC