Machine Learning for Earth Science Postdoctoral Research Associate

What You Will Do

The Computational Physics and Methods group (CAI-2) is seeking an outstanding candidate for a postdoctoral position at the intersection of machine learning, scientific computing, uncertainty quantification, and Earth system science.

The successful candidate will join a multidisciplinary team of mathematicians, physicists, Earth system scientists, and machine learning researchers advancing AI-enabled methods for complex Earth science problems. The postdoc will develop reusable machine learning capabilities for integrating heterogeneous models, simulations, observations, and reanalysis products across Arctic and high-latitude science applications. Core activities will include method development, scientific software implementation, empirical validation, and collaboration with domain scientists on mission-relevant problems involving predictability, risk, attribution, and multi-scale Earth system processes.

The position will emphasize composable AI/ML methods that connect process-based models, numerical simulations, observational datasets, and scientific workflows. Relevant methodological areas may include data-model fusion, surrogate modeling and emulation, probabilistic prediction, uncertainty quantification, data assimilation and state estimation, downscaling and upscaling, and causal modeling. The position offers exposure to multiple application domains, including ocean, sea ice, coastal hazards, terrestrial hydrology, permafrost, ice-sheet impacts, atmospheric extremes, and human-system risk, as well as opportunities for cross-disciplinary collaboration, scientific workshop organization, and conference participation.

What You Need

Minimum Job Requirements

* Experience in machine learning, scientific computing, data-driven modeling, or statistical methods for complex physical systems, as evidenced through a strong scientific record of peer-reviewed publications and presentations. *...