M²LInES: Multiscale Machine Learning In coupled Earth System Modeling
M²LInES: Multiscale Machine Learning In coupled Earth System Modeling Multiple positions are available as part of a new international project, M²LInES. The overall goal of the project is to improve climate projections and reduce climate model biases, especially at the air-sea interface, using machine learning. We will rely on data from a range of high-resolution (idealized and global) simulations and data assimilation products to deepen our understanding and improve the representation of subgrid physics in the ocean, sea-ice and atmosphere components of existing IPCC-class climate models. This is a highly collaborative project, and the researchers are expected to interact with different groups.