Postdoctoral Appointee - Data intensive Scientific Machine Learning

The Mathematics and Computer Science Division at Argonne National Laboratory seeks well-prepared candidates for a postdoctoral position in Data-intensive Scientific Machine Learning.

This is a collaborative project with Los Alamos National Laboratory, Illinois Institute of Technology, and Johns Hopkins University devoted to developing stable and robust deep-learning based forecasting for dynamical systems. In this role you will be performing foundational research by developing novel algorithms. In addition, and will leverage the developed algorithms to solve challenges related to robustness and stability for various critical applications such as modeling for the geophysical sciences and nuclear fusion.

Of key interest to this project are techniques related to neural ordinary differential equations, structure and volume preserving neural networks, generative models such as normalizing flows, etc. A strong background in the theory, analyses, and numerical methods for dynamical systems is important and experience with using HPC infrastructure is valuable. Candidates with experience in differential geometry and/or manifold learning would be particularly well-suited to this project. You will be expected to actively collaborate with computer scientists, physicists, mathematicians, and domain scientists, and will have the opportunity to build an independent research program.

The selected candidate will also have the opportunity to use state-of-the-art computing resources at the Argonne Leadership Computing Facility such as Aurora (, the planned exascale machine with >50,000 GPUs, and novel AI hardware such as Sambanova, Groq, Graphcore, Cerebras (

Position Requirements

  • PhD in computer science, mathematics, statistics, or a related discipline (completed within the last 3 years, or soon to be completed)
  • Graduate/postgraduate research in machine learning & dynamical systems; Programming experience in one of Tensorflow, Pytorch, JAX preferred

Desired skills:
  • Experience in reduced-order modeling, approximate inertial manifolds, differential geometry, manifold learning
  • Proposal development
  • Experience in interdisciplinary research involving computer scientists/mathematicians and discipline scientists
  • Ability to work well with other laboratories and universities, supercomputing centers and industry and ability to provide project leadership as well as have collaborative skills

Openings are available immediately, but there is flexibility in start dates. More information on foundational machine learning work at Argonne may be found at

Job Family
Postdoctoral Family

Job Profile
Postdoctoral Appointee

Worker Type
Long-Term (Fixed Term)

Time Type
Full time

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