Postdoc position in deep learning for climate science at NYU

[Carlos Fernandez-Granda]

The Courant Institute and the Center for Data Science at NYU have an open postdoctoral position in scientific machine learning as part of a new multi-institution international project, M2LInES https://m2lines.github.io. The scientific goal of this project is to improve climate predictions by reducing climate model errors using machine learning. 

The postdoc will work with Profs Joan Bruna, Carlos Fernandez-Granda and Laure Zanna on the development of generalizable deep learning algorithms with a focus on uncertainty quantification. Characterizing the uncertainty in the output of deep neural networks is a fundamental open question in machine learning, which is particularly critical in scientific applications. The postdoc will explore state-of-the-art approaches with the ultimate goal of improving ML models for climate processes (e,g., clouds, ocean mixing). An example of the kind of problems we are investigating can be found in this recent paper: https://www.essoar.org/doi/10.1002/essoar.10506419.1.

The position, available immediately, is a full-time appointment with the possibility of renewal for up to five years, subject to satisfactory performance.

Please apply here: https://apply.interfolio.com/89069