Four Postdoctoral Positions Available
Using Machine Learning for Bias Reduction in Climate Models
Princeton University
Princeton, New Jersey
Application deadline: 28 February 2021
For more information and to apply, go to:
https://puwebp.princeton.edu/AcadHire/apply/application.xhtml?listingId…
The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), invites applications for four postdoctoral scientists to conduct research on improving climate models. The initial appointment is for one year with the possibility of a second-year renewal subject to satisfactory performance and available funding.
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 will focus on the ocean and sea-ice components with four distinct areas of research:
- Development of machine-learned ocean model parameterizations trained on data from an ocean data-assimilation system;
- Development of machine-learned sea-ice parameterizations trained on data from a sea-ice data-assimilation system;
- Development of machine-learned ocean model parameterizations trained on process-study data, including large eddy simulations; and
- 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 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 postdoctoral researchers 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 strong background in physical oceanography, sea-ice science, data-assimilation, computer science, or a closely related field;
- Experience with ocean, sea-ice, climate models, or ocean/ice data-assimilation systems; and
- Experience, or demonstrated interest, in machine learning.
Candidates must have a PhD, preferably in oceanography or a closely related field.
Applications must include a cover letter, curriculum vitae, publication list, research statement (maximum of two pages including references), and three letters of recommendation.
For additional information about project 1, contact Dr. Feiyu Lu (feiyu.lu [at] princeton.edu); for project 2, contact Dr. Mitch Bushuk (mitchell.bushuk [at] noaa.gov); for project 3, contact Dr. Brandon Reichl (brandon.reichl [at] noaa.gov); and for project 4 or general queries contact, Dr. Alistair Adcroft (aadcroft [at] princeton.edu).
Application deadline: 28 February 2021
For more information and to apply, go to:
https://puwebp.princeton.edu/AcadHire/apply/application.xhtml?listingId…