The Courant Institute at New York University (NYU) is seeking expression of interest from a highly motivated Postdoctoral Associate to join our team working on improving the representation of sub-grid scale physics for climate models. This effort is part of the Learning Earth with Artificial Intelligence and Physics (LEAP), an NSF-funded Science and Technology Center. The scientific goal of this project is to enhance climate projections by reducing errors in climate models, specifically those rooted in the representation of the atmospheric boundary layer, using machine learning (ML).
The successful candidate will collaborate with Profs. Sara Shamekh (NYU) and Pierre Gentine (Columbia University), and with a team of interdisciplinary researchers, to develop and implement ML techniques for climate phenomena that span multiple scales and aspects of physics. This includes a focus on non-local boundary layer turbulence and shallow clouds.
This full-time appointment is available immediately. It is initially for one year, with the possibility of renewal for up to three years, subject to satisfactory performance and available funding.
In compliance with NYC’s Pay Transparency Act, the annual base salary range for this position is $62,500 - $70,000. New York University considers factors such as (but not limited to) the specific grant funding and the terms of the research grant when extending an offer.