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We are inviting applications for a Postdoctoral Associate Position at NYU Courant (https://cims.nyu.edu/dynamic/) and CDS (https://cds.nyu.edu) , to work on the physics of air-sea interaction, under the supervision of Prof Laure Zanna (https://laurezanna.github.io/) . The position is part of the recent National Science Foundation Learning the Earth with Artificial intelligence and Physics (LEAP) Science and Technology Center (STC), https://leap.columbia.edu/, a large multi-institutional center with a goal to improve climate projections using novel artificial intelligence for better climate adaptation.

Physical properties, such as momentum, heat, moisture, are exchanged at the air-sea interface and are essential for weather and climate. These exchanges -- or surface fluxes -- are determined empirically, using rather crude approximations, known as “ bulk formulae,” which relate large-scale quantities to fast and local exchanges of properties and constrained with somewhat spotty spatial observations. All observational estimates and climate model parameterizations rely on bulk formulas for air-sea fluxes. Based on bulk formulae, current observationally-derived estimates of the net heat flux into the ocean range from -10 W/m2 to 40 W/m2. The uncertainty in surface fluxes is an order of magnitude larger than the climate change signal (~ 1W/m2). In this project, we propose to rethink the parametrization of surface fluxes.

The postdoctoral associate will help develop a physics-informed, interpretable parameterization using a range of direct measurements (in situ and satellite-based), model output, and a combination of machine learning tools - in particular causal inference and equation discovery. The postdoctoral research will collaborate with a range of scientists at NYU, Columbia and NCAR.

Further information about equation-discovery for physics is discussed in a recent Quanta article (https://www.quantamagazine.org/machine-scientists-distill-the-laws-of-physics-from-raw-data-20220510/) , and details about LEAP’s goals are on our website (https://leap.columbia.edu) .

One of the primary goals of LEAP is to increase the diversity in geosciences and data science, to be on par with the US population by the end of the Science and Technology Center (STC) lifetime. The STC will take proactive steps to reach its diversity goal. We thus particularly welcome applicants from underrepresented groups.

The position, available immediately, is a full-time appointment, initially for one year, with the possibility of renewal for up to three years, subject to satisfactory performance and available funding.

In compliance with NYC’s Pay Transparency Act, the annual base salary range for this position is $58,500 - $90,000. New York University considers factors such as (but not limited to) the specific grant funding and the terms of the research grant when extending an offer.

+ Completion of a PhD in Atmospheric, Oceanic or Climate sciences, Physics, Applied Mathematics, Physics, Engineering, or a related field at the time of the appointment;

+ Strong background in climate science or related field;

+ Experience with statistical techniques;

+ Candidates should have a record of relevant publications in the peer-reviewed scientific literature appropriate to their career stage, and demonstrated effective written and verbal communication skills

Desirable

+ Experience in analysis of large datasets

+ Experience, or demonstrated interest, in machine learning

For full consideration, applicants should submit the following by 12/02/2022:

+ a Curriculum Vitae with a list of publications,

+ a cover letter (no more than 2 pages) detailing their research experience, how their interests would fit the current positions, career plans, and available start date,

+ 3 letters of recommendation

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