The Argonne Leadership Computing Facility (ALCF) is seeking a Postdoctoral Appointee to perform research and development of scalable workflow tools for coupling traditional HPC simulations with AI/ML.

This postdoctoral appointee will build upon work already being done at the Facility to enable such workflows and will contribute to the development and benchmarking of scalable solutions integrating parallel simulations with AI/ML distributed training and inferencing on leadership systems, such as the Aurora exascale supercomputer. Potential project areas include online training of robust AI/ML surrogates, AI driven workflows for efficient design space exploration and parameter optimization, cross-system workflows incorporating traditional HPC systems with novel AI accelerators, and developing performance benchmarks for evaluating hardware on current and future HPC systems. 

In this role, you can expect to:

• Collaborate with scientists and researchers across multiple scientific domains and Argonne divisions, as well as other national labs and industry partners
• Contribute to open-source software used on platforms ranging from laptops to the world’s largest supercomputers
• Present research findings through journal publications, conference presentations, workshops, and other means
• Refine their skills and knowledge related to HPC and AI for science, including large scale distributed training, active learning and fine-tuning

Position Requirements

Required skills and qualifications:

• Ph.D. (completed within the last 0-5 years or soon-to-be-completed in 2024) in computer science, applied mathematics, physics, chemistry, or similar fields
• Experience contributing to scientific software in C++, Fortran, Python, or comparable languages
• Familiarity with building and deploying software on HPC systems
• Effective written and oral communication skills
• Ability to work individually and collaboratively as part of a team
• Ability to model Argonne’s core values: Impact, Safety, Respect, Integrity, and Teamwork

Preferred skills and qualifications:

• Demonstrated experience with parallel scientific computing (e.g., MPI, OpenMP, CUDA, etc.) or high-throughput computing on leadership class HPC systems
• Experience with machine learning methods and frameworks (e.g., TensorFlow, PyTorch, Jax, etc.), especially applied to scientific problems
• Experience with distributed ML training and inference tasks on multiple GPUs
• Experience with high-throughput computing or running large job ensembles on HPC systems

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