Columbia Engineering, the Fu Foundation School of Engineering and Applied Science at Columbia University in the City of New York invites applications for an Associate Research Scientist in the field of global atmospheric modeling and machine learning, under the supervision of Pierre Gentine at Columbia University. The position is part of the National Science Foundation-funded Learning the Earth with Artificial intelligence and Physics (LEAP) Science and Technology Center (STC), https://leap.columbia.edu/, a multi-institutional center effort meant to improve climate projections using novel artificial intelligence for better climate adaptation. This position will be based at LEAP’s offices in New York City.

The goal of this project is to develop a new generation of atmospheric models for Earth system models, using differentiable models and AI agents. The first objective of the position will be to refactor existing codes so that they are fully differentiable, written in high-level language but also scalpel on GPUs. In the second part of the position, the ARS will use the new code to develop new machine learning parameterizations of the atmospheric boundary layer and of convection. 

This ARS will work with LEAP and NCAR scientists to build the workflows that allow for rapid production, analysis, and emulation of PPEs and to disseminate findings to CESM developers and the wider research community. 

The ARS will closely collaborate with members of the Atmospheric Modeling and Predictability Section in the Climate and Global Dynamics Laboratory at NCAR as well as with graduate students, postdocs, and other staff within LEAP. The ARS will also collaborate with the M2LInES project led by Prof. Laure Zanna at NYU.  

A second, important, aim of the project is to establish more general support and coordination of LEAP-developed machine learning activities, including conducting and analyzing experiments using ML-based parameterizations and emulators as well as explorations of methods to generate high-quality training data sets for additional ML-based schemes. 

We are committed to building a community of scientists with a range of academic and professional backgrounds, and believe that a variety of perspectives and experiences is essential to advancing our research and mission.

Minimum Qualifications:

A Ph.D. in Atmospheric Science, Data Science, Computer Science, Physics, Earth System Science or a directly related discipline is required by the start of the appointment.

Strong programming skills are a requirement.

Preferred Qualifications:

Post-doctoral experience and demonstrated experience in Earth System Science, Data Science, or similar.

Fluency in Python.

Familiarity with Fortran.

Advanced experience in machine learning.

Some experience with AI agents. 

Demonstrated experience in statistical/mathematical analyses of model output and/or observational datasets.

Experience running and analyzing global climate simulations on high performance computing platforms   

Excellent command of the English language (verbal and written) and strong communication skills are desired.