Postdoctoral Appointee - High Performance Workflows

The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing facilities in partnership with the computational science community. We help researchers solve some of the world’s largest and most complex problems with our unique combination of supercomputing resources and computational science expertise.

ALCF’s data science group seeks a post-doctoral appointee to perform research and development on computational workflows in the context of machine- and deep-learning, computational science simulation and analysis, and in coupling of learning and simulation.

In this role, you will:

• Work with a wide variety of science applications with the common theme of scaling on current ALCF systems and preparing for future exascale systems.
• Collaborate with diverse colleagues and researchers from the data science team and with computer scientists and domain scientists within and outside Argonne.
• Expected to present and publish their work at major conferences and journals.
• Leverage Python to facilitate workflows running on current and future supercomputers at ALCF. This will involve ensembles of large-scale parallel jobs and high-throughput computing to reach high efficiency, requiring an understanding of data transfer, I/O intensity, job schedulers, application performance, and novel computing hardware.
• Expect your work to span multiple dimensions, including a wide variety of science domains, a mix of simulation and analysis, applications written in multiple languages and exhibiting a range of execution profiles, significant data challenges, machine learning and deep learning, all running on multiple computing architectures.
  

Position Requirements

• Ph.D. + 0-3 years of experience in computer science, computational science, or a related field
• Extensive experience with Python programming and key libraries (NumPy, SciPy, TensorFlow, PyTorch)
• Extensive experience with computational workflows on large-scale systems
• Experience related to parallel algorithms, I/O architectures, and performance evaluation and tuning
• Experience with API development
• Ability to quickly assimilate new subject areas and effectively interact with experts in those areas
• Good written and communication skills
• Good experience and skills in interdisciplinary teams involving mathematicians, computer scientists, and discipline scientists
• Good publication record, preferably including first-author publication(s)
• Collaborative skills including the ability to work well with other laboratories and universities, supercomputer centers, and industry

Application Documents

• Cover letter (recommended); uploaded as a PDF document.
• Resume
• 3 Letters of Recommendation

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