Postdoctoral Research Associate at Princeton University in Princeton, New Jersey

Job Description:

Position: Postdoctoral Research Associate

Description: Improving Ocean Surface Boundary Layer Mixing Parameterizations with Langmuir Turbulence and Machine Learning 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 research scientists to develop machine learned parameterizations for vertical mixing in the ocean surface boundary layer and apply them to ocean climate models. Our previous work demonstrated that neural networks can learn to predict the vertical structure of vertical diffusivity and the networks can then be applied in an ocean climate model to improve simulations of upper ocean stratification (https://dx.doi.org/10.1029/2023MS003890). The successful applicant for this position will work to advance these ideas, specifically focusing on improving representation of the effect of ocean surface waves and Langmuir turbulence on the energetics and vertical distribution of mixing within the ocean surface boundary layer. This work will involve: i) contributing to design and run new Large Eddy Simulation experiments; ii) and analyzing the LES output to generate training data; iii) using Machine Learning techniques to learn improvements to the existing mixing parameterization; iv) implementing the new schemes in a global circulation model (MOM6); v) and finally, evaluating the impacts of the new parameterization on ocean climate simulation in a global 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) familiarity with concepts of geophysical fluids, such as in atmospheric science or physical oceanography; b) experience with numerical modeling including Large Eddy Simulation, turbulence closure methods, and/or atmospheric or oceanic circulation/climate models; and c) experience, or demonstrated interest, in machine learning. Candidates must have a Ph.D. or expect to complete a Ph.D. for an anticipated start date in late 2024 or early 2025. 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 November 15th, 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. Brandon Reichl (brandon.reichl@noaa.gov) or Dr. Alistair Adcroft (aadcroft@princeton.edu). The work location for this position is in-person on campus at Princeton University. 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. 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.

PI250992148

Salary:

$1.00

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