The Mathematics and Computer Science Division at Argonne National Laboratory seeks well-prepared candidates for a postdoctoral position in scientific machine learning. The successful candidate will be performing theoretical, foundational, and applied machine learning research in reinforcement learning, higher-order graph neural networks, and/or surrogate-based mixed integer optimization for scientific applications and leveraging the developed methods to solve various learning challenges posed by Department of Energy scientific applications. The class of methods pursued is flexible based on a wide range of applications such as climate, nuclear physics, fusion science, and distributed infrastructures. The research will include scientific applications such as climate, nuclear physics, fusion science, and distributed infrastructures. The successful candidate will actively collaborate with computer scientists, mathematicians, and domain scientists and have the opportunity to build an independent research program.
The successful candidate will have the opportunity to use advanced supercomputers across the Department of Energy computing facilities such as planned Aurora (>50,000 GPUs) exascale system at the Argonne Leadership Computing Facility (https://www.alcf.anl.gov/aurora) as well as emerging novel AI accelerators from various hardware vendors such as Cerebras, SambaNova, Graphcore, and Groq (https://ai.alcf.anl.gov/).
- PhD in computer science, mathematics, statistics, or a related discipline (completed within the last 3 years, or soon to be completed) with strong background in one or more of the following: Reinforcement learning, Geometric learning (graph neural networks), or related area.
- Graduate/postgraduate research in machine learning, computer science, and/or mathematical optimization.
- Strong development skills in deep learning frameworks (Tensorflow, Pytorch, JAX/Flax)
- Experience in interdisciplinary research involving computer scientists/mathematicians and discipline scientists
- Ability to work well with other laboratories and universities, supercomputer centers and industry
- Ability to provide project leadership as well as have collaborative skills
- Machine learning on high-performance computing systems and AI accelerators.
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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