New York University

Description

The Courant Institute at New York University (NYU) and the Center for Data Science (CDS) seek highly motivated postdoctoral researchers to work on a new generation of global climate models augmented with AI, as part of the international project, M²LInES https://m2lines.github.io. The scientific goal of this project is to improve climate predictions by reducing climate model errors at the air-sea/ice interface using machine learning (ML) models. The successful candidate will explore the impact of new ML parameterizations on the climate in a range of climate simulations. 

Read more about our recent progress summarized here or our publications. 

The successful candidate will work with Profs Laure Zanna & Carlos Fernandez-Granda and their groups at NYU, together with the GFDL/Princeton and NCAR groups. We are looking for candidates interested in exploring at least one of the following ideas: 

• The dynamics of key ocean and climate variables, e.g., sea level or sea surface temperature. This includes variability and forced response in the new models (e.g., CM4, OM4 augmented with ML parametrizations), and comparison to other existing simulations (e.g., CMIP, HighResMIP). 
• The role of ocean dynamics and the pattern effect in coupled idealized simulations (e.g., Wu et al., 2021), including sensitivity to subgrid ML physics (e.g., new parameterizations, perturbed parameter ensemble).
• ML-driven assessment of climate models and extremes: new ML metrics for evaluating climate models or new downscaling techniques for evaluating against observations or high-resolution models (e.g., Mardani et al., 2023).

The position, available immediately, is a full-time appointment, initially for one year, with the possibility of renewal for up to 3 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 - $125,000. 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.

Qualifications

• Completion of a PhD in physics, mathematics, computer science, engineering, statistics, or a related field at the time of the appointment; 
• Programming and/or data analysis experience;
• Interest in the application of machine learning to science and engineering problems;
• A solid record of relevant publications in the peer-reviewed scientific literature appropriate to their career stage; 
• Ability to work independently and as part of an interdisciplinary team; 
• Ability to work in a fast-paced environment; 
• Strong communication skills.