Posting for a collegue with whom the MIT Julia Lab actively collaborates with. The project is around learning universal differential equation representation of generation behavior from agent-based models. (This is actually a second posting, i.e. a new position since a similar call was put out and hired for last May 2022!)
Sandia National Laboratories’ Computational Data Science department seeks a postdoctoral researcher excited to develop and apply novel machine learning methods to agent-based epidemiological models. These models support enhanced and accelerated pandemic response and both Sandia’s fundamental research and national security missions.
The research will develop explainable, data-driven surrogate models that capture complex, multi-scale disease progression dynamics by combining scientific computing and machine learning (Scientific Machine Learning). The position requires deep knowledge of machine learning (ML) and its use in the modeling of nonlinear dynamical systems, dimensionality reduction, and some knowledge of statistical inference and spatial modeling.
The candidate must be able to conduct original research (as evidenced by publications) and be adept at programming (as evidenced by code development expertise). The work will be performed with an interdisciplinary research team spread over academia, Lawrence Livermore National Laboratory and Sandia. The research team’s strengths lie in computational mathematics, computational science, high performance computing, scientific software development, uncertainty quantification, and machine learning algorithms.