POSTDOCTORAL ASSOCIATE IN ATMOSPHERIC CONVECTION AND MACHINE LEARNING, Earth, Atmospheric and Planetary Sciencs (EAPS), to assume a position at the interface of climate science and machine learning. Will work with Professor Paul O'Gorman on the development of subgrid parameterizations of atmospheric convection based on machine learning and implementation of the parameterizations in climate models. This work is part of the M2LInES project and offers opportunities for collaboration within that project. Additional information is available at http://pog.mit.edu/research.html.

 

Job Requirements

REQUIRED: a Ph.D. in geosciences, physics, engineering, or a related field at the time of the appointment; strong background in atmospheric dynamics, climate modeling, and/or machine learning; and scientific programming experience. Job #22127

In addition to applying online via the MIT website, applicants are asked to send a CV, a brief statement of research interests, and the names and email addresses of three references to pog@mit.edu with ‘postdoc job’ in the subject line. Prospective applicants may contact Professor O’Gorman (pog@mit.edu) with any questions regarding the nature of the research to be conducted.

The initial appointment will be for one year, with the possibility of extension depending on performance and availability of funding.

Applications will be considered upon receipt and until the position is filled.