We are looking for a postdoc to work at the intersection of machine learning and numerical climate models. The work will be conducted in the Atmospheric and Oceanic Sciences program in conjunction with a leading climate lab (NOAA-GFDL) and is part of a collaborative project (M²LInES). The overall goal of the project is to use machine learning to reduce biases in existing climate models. This postdoc research is about aspects of the implementation, and will include assisting in the training of learned parameterizations, implementing learned parameterizations in climate models, and understanding the numerical stability of learned parameterizations. This work will start with MOM6 but will work with other models later in the project. Find out more and apply at Application for Postdoctoral Research Associate
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