A postdoctoral research position is available in Machine Learning Methods in Atmospheric Sciences at the Courant Institute of Mathematical Sciences as part of a research effort on the applications of data-driven methods to solve multi-scale problems in physical sciences. We seek an outstanding data scientist and/or physical scientist interested in investigating novel approaches to the scientific modeling of natural phenomena across different disciplines including climate science, solid state physics, material science and urban engineering.
The successful candidate will work under the supervision of Prof. O. Pauluis (Courant Instutute) and will collaborate directly with scientists at New York University, University of Illinois at Urbana-Champaign, University of Minnesota, SUNY Binghampton and the US Naval Academy.
The duties include the conceptualization and development of machine learning approaches relevant to atmospheric flows, their implementation in numerical codes, and the dissemination of these results to scientific presentation and articles.
Start date is negotiable, with a target of January 1, 2020. The initial term of the appointments will be one year, renewable for a second year. A competitive salary will be offered, plus benefits. Research funds are also available. Please submit a CV, a 2-4 page research statement, and names and contact information for at least three references.
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