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<span style="font-family: "Times New Roman", serif;">The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral </span><span style="font-family: "Times New Roman", serif;">or
more senior </span><span style="font-family: "Times New Roman", serif;">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.</span><span style="font-family: "Times New Roman", serif;"><o:p></o:p></span></p>
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<span style="font-family: "Times New Roman", serif;">The work is part of a larger project, M<sup>2</sup>LInES (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 M<sup>2</sup>LInES project across multiple institutions.</span><span style="font-family: "Times New Roman", serif;"><o:p></o:p></span></p>
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<span style="font-family: "Times New Roman", serif;">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.</span><span style="font-family: "Times New Roman", serif;"><o:p></o:p></span></p>
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<span style="font-family: "Times New Roman", serif;">Candidates must have a Ph.D. and preferably in oceanography, geophysical fluid dynamics, computer science, or a closely related field. </span><span style="font-family: "Times New Roman", serif;">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.<o:p></o:p></span></p>
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<span style="font-family: "Times New Roman", serif;">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.</span><span style="font-family: "Times New Roman", serif;"><o:p></o:p></span></p>
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<span style="font-family: "Times New Roman", serif;">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 <b>%listings_link%</b>. For
additional information contact Dr. Alistair Adcroft (aadcroft@princeton.edu).</span><span style="font-family: "Times New Roman", serif;"><o:p></o:p></span></p>
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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. <a name="_Hlk148005940"><font color="#000000">The work location for
this position is in-person on campus at Princeton University.</font><o:p></o:p></a></p>
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<span style="font-family: "Times New Roman", serif;">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.</span><span style="font-family: "Times New Roman", serif;"><o:p></o:p></span></p>
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<span style="font-size: 14px;">Anna Valerio :8)</span><br>
<span style="font-size: 14px;"><i>Department & Graduate Administrator</i></span><br>
<font color="#ff9300" style="font-size: 14px;">Princeton University</font><br>
<span style="font-size: 14px;">AOS Program</span><br>
<span style="font-size: 14px;">300 Forrestal Road, 209 Sayre Hall</span><br>
<span style="font-size: 14px;">Princeton, NJ 08540</span><br>
<span style="font-size: 14px;">phone: 609-258-6677<br>
fax: 609-258-2850<br>
e-mail: apval@princeton.edu<br>
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