Ocean Surface Boundary Layer Mixing Parameterizations using Machine Learning
Princeton University
Princeton, NJ
Full-time
Information Services / Technology (IT)
Posted on April 12, 2024
Position: Ocean Surface Boundary Layer Mixing Parameterizations using Machine Learning
Company: Princeton University
Description: 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 developing and using machine learned parameterizations for mixing in the ocean surface boundary layer. Our previous work has demonstrated the utility of using neural networks to improve ad-hoc components of the existing ocean surface boundary layer mixing parameterization in the GFDL ocean climate model (https://dx.doi.org/10.1029/2023MS003890). The new research will build on this work, specifically devoted to further improving other poorly constrained processes with the aid of a set of Large Eddy Simulations and other higher-order turbulence closure methods. This work will involve running new simulations to generate training data, using Machine Learning techniques to infer modifications to the mixing parameterizations, implementing the scheme in a global circulation model (MOM6), and evaluating the new parameterization within a global-scale climate model. 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 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 turbulence closure parameterizations; 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 April 15, 2024, 11:59 pm EST for full consideration. Applicants should apply online to %listings_link%. Princeton is interested in candidates who, through their research, will contribute to the diversity and excellence of the academic community. For additional information contact Dr. Brandon Reichl (brandon.reichl@noaa.gov) or Dr. Alistair Adcroft (aadcroft@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.
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