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<span style="font-size: 12pt; line-height: 18.4px; 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 or
more senior scientist to conduct research on developing and using machine learned parameterizations for mixing in the ocean surface boundary layer.<span style="letter-spacing: 2pt;"> </span>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<span style="letter-spacing: -0.35pt;"> </span>in<span style="letter-spacing: -0.35pt;"> </span>the<span style="letter-spacing: -0.35pt;"> </span>GFDL<span style="letter-spacing: -0.35pt;"> </span>ocean<span style="letter-spacing: -0.35pt;"> </span>climate<span style="letter-spacing: -0.35pt;"> </span>model<span style="letter-spacing: -0.35pt;"> </span>(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<span style="letter-spacing: -0.15pt;"> </span>closure<span style="letter-spacing: -0.15pt;"> </span>methods.<span style="letter-spacing: 2pt;"> </span>This<span style="letter-spacing: -0.15pt;"> </span>work<span style="letter-spacing: -0.15pt;"> </span>will<span style="letter-spacing: -0.15pt;"> </span>involve<span style="letter-spacing: -0.15pt;"></span>running<span style="letter-spacing: -0.15pt;"> </span>new<span style="letter-spacing: -0.15pt;"> </span>simulations<span style="letter-spacing: -0.15pt;"> </span>to<span style="letter-spacing: -0.15pt;"> </span>generate<span style="letter-spacing: -0.15pt;"> </span>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.<o:p></o:p></span></p>
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<span style="font-size: 12pt; line-height: 18.4px; font-family: "Times New Roman", serif;">The<span style="letter-spacing: -0.15pt;"> </span>work<span style="letter-spacing: -0.15pt;"> </span>is<span style="letter-spacing: -0.15pt;"> </span>part<span style="letter-spacing: -0.15pt;"> </span>of<span style="letter-spacing: -0.15pt;"> </span>a<span style="letter-spacing: -0.15pt;"> </span>larger<span style="letter-spacing: -0.15pt;"> </span>project,<span style="letter-spacing: -0.15pt;"> </span>M2LInES,<span style="letter-spacing: -0.15pt;"> </span>covering<span style="letter-spacing: -0.15pt;"> </span>eleven<span style="letter-spacing: -0.15pt;"> </span>institutions.<span style="letter-spacing: -0.15pt;"> </span>The<span style="letter-spacing: -0.15pt;"> </span>overall<span style="letter-spacing: -0.15pt;"> </span>goal<span style="letter-spacing: -0.15pt;"> </span>is<span style="letter-spacing: -0.15pt;"> </span>to
reduce climate model biases at the air-sea/ice interface by improving subgrid physics in the ocean,<span style="letter-spacing: -0.15pt;"> </span>sea<span style="letter-spacing: -0.15pt;"> </span>ice<span style="letter-spacing: -0.15pt;"> </span>and<span style="letter-spacing: -0.15pt;"> </span>atmosphere<span style="letter-spacing: -0.15pt;"> </span>components<span style="letter-spacing: -0.15pt;"> </span>of<span style="letter-spacing: -0.15pt;"></span>existing<span style="letter-spacing: -0.15pt;"> </span>coarse<span style="letter-spacing: -0.15pt;"> </span>(¼°<span style="letter-spacing: -0.15pt;"> </span>to<span style="letter-spacing: -0.15pt;"> </span>1°)<span style="letter-spacing: -0.15pt;"> </span>resolution<span style="letter-spacing: -0.15pt;"> </span>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.<o:p></o:p></span></p>
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<span style="font-size: 12pt; line-height: 18.4px; font-family: "Times New Roman", serif;">In<span style="letter-spacing: -0.15pt;"> </span>addition<span style="letter-spacing: -0.15pt;"> </span>to<span style="letter-spacing: -0.15pt;"> </span>a<span style="letter-spacing: -0.15pt;"> </span>quantitative<span style="letter-spacing: -0.15pt;"> </span>background,<span style="letter-spacing: -0.15pt;"> </span>the<span style="letter-spacing: -0.15pt;"> </span>selected<span style="letter-spacing: -0.15pt;"> </span>candidates<span style="letter-spacing: -0.15pt;"> </span>will<span style="letter-spacing: -0.15pt;"> </span>ideally<span style="letter-spacing: -0.15pt;"> </span>have<span style="letter-spacing: -0.15pt;"> </span>one<span style="letter-spacing: -0.15pt;"> </span>or<span style="letter-spacing: -0.15pt;"> </span>more<span style="letter-spacing: -0.15pt;"> </span>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.<o:p></o:p></span></p>
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<span style="font-size: 12pt; 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. 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-size: 12pt; line-height: 18.4px; font-family: "Times New Roman", serif;">Complete<span style="letter-spacing: -0.35pt;"> </span>applications,<span style="letter-spacing: -0.35pt;"> </span>including<span style="letter-spacing: -0.35pt;"> </span>a<span style="letter-spacing: -0.35pt;"> </span>cover<span style="letter-spacing: -0.35pt;"> </span>letter,<span style="letter-spacing: -0.35pt;"> </span>CV,<span style="letter-spacing: -0.35pt;"> </span>publication<span style="letter-spacing: -0.35pt;"> </span>list,<span style="letter-spacing: -0.35pt;"> </span>research<span style="letter-spacing: -0.35pt;"> </span>statement<span style="letter-spacing: -0.35pt;"> </span>(no<span style="letter-spacing: -0.35pt;"> </span>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.<o:p></o:p></span></p>
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<span style="font-size: 12pt; line-height: 18.4px; font-family: "Times New Roman", serif;">Applicants<span style="letter-spacing: -0.05pt;"> </span>should<span style="letter-spacing: -0.05pt;"> </span>apply<span style="letter-spacing: -0.05pt;"> </span>online<span style="letter-spacing: -0.05pt;"> </span>at
https://www.princeton.edu/acad-positions/position/33961. Princeton<span style="letter-spacing: -0.2pt;"> </span>is<span style="letter-spacing: -0.2pt;"> </span>interested<span style="letter-spacing: -0.2pt;"> </span>in<span style="letter-spacing: -0.2pt;"></span>candidates<span style="letter-spacing: -0.2pt;"> </span>who,<span style="letter-spacing: -0.2pt;"> </span>through<span style="letter-spacing: -0.2pt;"> </span>their<span style="letter-spacing: -0.2pt;"> </span>research,<span style="letter-spacing: -0.2pt;"> </span>will<span style="letter-spacing: -0.2pt;"> </span>contribute<span style="letter-spacing: -0.2pt;"> </span>to<span style="letter-spacing: -0.2pt;"> </span>the<span style="letter-spacing: -0.2pt;"> </span>diversity
and<span style="letter-spacing: -0.05pt;"> </span>excellence<span style="letter-spacing: -0.05pt;"> </span>of<span style="letter-spacing: -0.05pt;"> </span>the<span style="letter-spacing: -0.05pt;"> </span>academic<span style="letter-spacing: -0.05pt;"> </span>community.<span style="letter-spacing: -0.05pt;"> </span>For<span style="letter-spacing: -0.35pt;"></span>additional<span style="letter-spacing: -0.35pt;"> </span>information<span style="letter-spacing: -0.35pt;"> </span>contact<span style="letter-spacing: -0.35pt;"> </span>Dr.<span style="letter-spacing: -0.35pt;"> </span>Brandon<span style="letter-spacing: -0.35pt;"> </span>Reichl<span style="letter-spacing: -0.35pt;"> </span>(brandon.reichl@noaa.gov)<span style="letter-spacing: -0.35pt;"> </span>or<span style="letter-spacing: -0.35pt;"> </span>Dr.<span style="letter-spacing: -0.35pt;"> </span>Alistair
Adcroft (aadcroft@princeton.edu).<o:p></o:p></span></p>
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This<span style="letter-spacing: -0.2pt;"> </span>position<span style="letter-spacing: -0.2pt;"> </span>is<span style="letter-spacing: -0.2pt;"> </span>subject<span style="letter-spacing: -0.2pt;"> </span>to<span style="letter-spacing: -0.2pt;"> </span>Princeton<span style="letter-spacing: -0.2pt;"> </span>University's<span style="letter-spacing: -0.2pt;"> </span>background<span style="letter-spacing: -0.2pt;"> </span>check<span style="letter-spacing: -0.2pt;"> </span>policy<span style="letter-spacing: -0.2pt;"> </span>which<span style="letter-spacing: -0.2pt;"> </span>will<span style="letter-spacing: -0.2pt;"> </span>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-size: 12pt; line-height: 18.4px; font-family: "Times New Roman", serif;"> Princeton<span style="letter-spacing: -0.05pt;"> </span>University<span style="letter-spacing: -0.05pt;"> </span>is<span style="letter-spacing: -0.05pt;"> </span>an<span style="letter-spacing: -0.05pt;"> </span>equal<span style="letter-spacing: -0.05pt;"> </span>opportunity/affirmative<span style="letter-spacing: -0.05pt;"> </span>action<span style="letter-spacing: -0.05pt;"> </span>employer<span style="letter-spacing: -0.05pt;"> </span>and<span style="letter-spacing: -0.05pt;"> </span>all
qualified<span style="letter-spacing: -0.25pt;"> </span>applicants<span style="letter-spacing: -0.25pt;"> </span>will<span style="letter-spacing: -0.25pt;"> </span>receive<span style="letter-spacing: -0.25pt;"> </span>consideration<span style="letter-spacing: -0.25pt;"> </span>for<span style="letter-spacing: -0.25pt;"> </span>employment<span style="letter-spacing: -0.25pt;"> </span>without<span style="letter-spacing: -0.25pt;"> </span>regard<span style="letter-spacing: -0.25pt;"> </span>to<span style="letter-spacing: -0.25pt;"> </span>age,<span style="letter-spacing: -0.25pt;"> </span>race,<span style="letter-spacing: -0.25pt;"> </span>color,
religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.<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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