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Postdoctoral Researcher

2 months ago

Lemont, Illinois, United States Light Sources Full time
Job Summary

We are seeking a highly skilled Postdoctoral Appointee to join our team at Light Sources, working at the intersection of scientific machine learning (SciML) and large-scale simulation codes.

Key Responsibilities
  • Design and develop machine learning kernels as local operators, embedding them within large-scale simulation codes.
  • Couple simulations with machine learning algorithms, enabling simultaneous training of these components within the simulation environment.
  • Explore the use of advanced solvers like PETSc for these developments, expanding their SciML applicability to a wider range of scientific applications.
  • Collaborate with scientists from multiple domains to enhance simulation speeds and accuracy through machine learning techniques.
Requirements
  • Ph.D. (completed within the last 0-5 years) in applied mathematics, physics, computer science, mechanical engineering, aerospace engineering, or similar fields.
  • Experience contributing to scientific software in C, C++, Python, or comparable languages.
  • Strong background in computational fluid dynamics (CFD), machine learning (ML), and high-performance computing (HPC).
  • Collaborative skills, including working well with other scientists, divisions, laboratories, and universities.
  • Effective oral and written communication skills with all levels of the organization.
  • Ability to model our core values: Impact, Safety, Respect, Integrity, and Teamwork.
Preferred Qualifications
  • Demonstrated experience with parallel algorithms, for example, using frameworks like MPI, OpenMP, CUDA, Kokkos, SYCL, or similar.
  • Demonstrated experience running applications at scale (> 50 computing nodes).
  • Experience with PETSc (or other scientific libraries used as external solvers) and its integration with ML frameworks such as PyTorch.
  • Knowledge of subgrid-scale models and closure models.
  • Experience with computing gradients for PDE-constrained optimization problems, adjoint calculations, and backpropagation.
  • Ability to address dynamic stability issues of hybrid systems during training and operational phases.