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Postdoctoral Positions: Using Machine Learning for Bias Reduction in Climate Models
Princeton University in Princeton, New Jersey
 
 
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Date Posted 01/22/2021
Category
Admin-Tutors and Learning Resources
Employment Type
Application Deadline Open until filled
 
 
 
 
 
The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks four postdoctoral scientists to conduct research on improving climate models. The work is part of a larger project, M2LInES, 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 ( to 1) resolution IPCC-class climate models, and their coupling, using machine learning. The research at Princeton University/GFDL will focus on the ocean and sea-ice components with four distinct areas of research: 1) Development of machine-learned ocean model parameterizations trained on data from an ocean data-assimilation system; 2) Development of machine-learned sea-ice parameterizations trained on data from a sea-ice data-assimilation system; 3) Development of machine-learned ocean model parameterizations trained on process-study data, including large eddy simulations; 4) Implementation of existing machine-learned parameterizations in the ocean model and development and implementation of machine-learning algorithms in both the ocean and sea-ice components of the GFDL climate 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 postdocs will be expected to collaborate with each other and with other members of the M2LInES project.

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

Candidates must have a Ph.D. and preferably in Oceanography, or a closely related 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 February 28, 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 %listings_link%.  For additional information about project 1 contact Dr. Feiyu Lu (feiyu.lu@princeton.edu) for project 2 contact Dr. Mitch Bushuk (mitchell.bushuk@noaa.gov), for project 3 contact Dr. Brandon Reichl (brandon.reichl@noaa.gov), and for project 4 or general queries contact, Dr. Alistair Adcroft (aadcroft@princeton.edu).

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.
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