[ES_JOBS_NET] Postdoctoral Position: Interfacing machine learning with climate models, Princeton, NJ

[Anna P. Valerio]

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 on the use of machine learning in ocean climate models. The goal is to demonstrate the successful use of machine learned parameterizations of unresolved processes that will reduce biases in realistic ocean circulation models and climate models. The research entails implementation of existing trained parameterizations, development of new parameterizations, and assessment of ocean and climate simulations, and run-tme training of networks. These will be implemented and evaluated in newer versions of the global circulation model OM4, the climate model CM4, and the ocean code MOM6.

The work is part of a larger project, M2LInES (https://m2lines.github.io), covering eleven institutions. The overall goal is to reduce climate model biases at the air-sea/ice interface by improving subgrid physics in the ocean, sea ice and atmosphere components of existing coarse (¼° to 1°) resolution IPCC-class climate models, and their coupling, using machine learning. This part of the research at Princeton University/GFDL will involve working with the SPEAR ocean data assimilation system and the MOM6 ocean circulation model. The prognostic parameterizations will be state-dependent and trained to minimize model-observation misfits with the aim of reducing inherent biases in free-running climate simulations. The research will require analysis and interpretation of model output, the management of large datasets and the application of neural nets or other machine learning techniques to those data. The postdoc will be expected to collaborate with other postdocs at Princeton and with other members of the M2LInES project across multiple institutions.

In addition to a quantitative background, the selected candidates will ideally have one or more of the following attributes: a) a background in physical oceanography, or machine learning, or a closely related field; b) experience with ocean-circulation or climate models, or ocean data-assimilation systems; and c) experience, or demonstrated interest, in machine learning.

Candidates must have a Ph.D. and preferably in oceanography, geophysical fluid dynamics, computer science, or a closely related field. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments.
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 March 31, 2024, 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 %listings_link%. For additional information contact Dr. Alistair Adcroft (aadcroft at princeton.edu).

This position is subject to Princeton University's background check policy which will include meeting the security requirements for accessing the NOAA Geophysical Fluid Dynamics Laboratory. The work location for this position is in-person on campus at Princeton University.
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.

Anna Valerio :8)
Department & Graduate Administrator
Princeton University
AOS Program
300 Forrestal Road, 209 Sayre Hall
Princeton, NJ 08540
phone:  609-258-6677
fax: 609-258-2850
e-mail:  apval at princeton.edu





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