The Bortz-Dukic group in Applied Mathematics at the University of Colorado, Boulder is hiring up to two postdoctoral researchers. We develop, analyze, and apply weak-form SciML methods (WSINDy, WENDy, etc.) for noise-robust equation learning, parameter estimation, and reduced-order modeling learned directly from data. Current applications include nuclear fusion, weather, imaging, mathematical ecology, collective cell migration, epidemiology, aging, and mitochondrial dynamics. We collaborate with scientists and researchers at several national labs, and the positions are supported by active NIH-NIGMS, NSF, and DOE funding.

Postdocs in the group are expected to perform research and publish in top journals, as well as help mentor graduate and in some instances undergraduate students. Teaching applied math courses is an option for those interested. We are looking for someone with strong training in one or more of the mathematical sciences (numerical analysis, functional analysis, dynamical systems, statistics, etc.) and research experience in at least one of: data-driven modeling and equation learning, sparse regression and model selection, reduced-order or closure modeling, or multi-scale methods. Prior experience with weak-form methods is helpful but not required.