The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral or more senior scientist to conduct research at the intersection between computer science and climate modeling. The research involves implementation of existing machine-learned parameterizations in an ocean model, development and implementation of new machine-learned architectures in ocean and sea-ice components of the GFDL climate model, and later in an atmospheric model. The focus will include building high performance learning systems, coupling deep learning methods with equation-based simulation models. We will also study the numerical stability of learned parameterizations and examine the need for a full end-to-end training strategy (using the full model within a data-assimilation context) versus an offline training approach. The work is part of a larger project, MLInES, covering eleven institutions (https://m2lines.github.io). The overall goal of MLInES is to reduce climate model biases at the air-sea/ice interface by using machine learning to improve the representation of subgrid physics in the ocean, sea ice and atmosphere components of some existing IPCC-class climate models. It is expected there will be close collaboration with the other postdocs that have been hired through MLInES at Princeton, and throughout the broader MLInES project.
In addition to a quantitative background, the selected candidate will ideally have one or more of the following attributes: a) a strong background in computer science, applied mathematics, computational fluid dynamics, data-assimilation, numerical methods, or a closely related field, b) experience with machine-learning, and/or ocean, sea-ice, climate models, earth-system data-assimilation systems.
A Ph.D. is required, preferably in a quantitative field. The initial appointment is for one year with the possibility of a second-year renewal subject to satisfactory performance and available funding.
Complete applications, including a cover letter, CV, publication list, research statement (no more than 2 pages incl. references), and 3 letters of recommendation should be submitted by July 15th, 2021, 11:59 pm EST for full consideration. Princeton is interested in candidates who, through their research, will contribute to the diversity and excellence of the academic community.
Applicants should apply online to https://puwebp.princeton.edu/AcadHire/position/20861. For additional information contact Dr. Alistair Adcroft (firstname.lastname@example.org), Dr V Balaji (email@example.com) and Prof. Laure Zanna (firstname.lastname@example.org).
This position is subject to Princeton University's background check policy.
Princeton University is an equal opportunity/affirmative action employer, and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.