Associate Research Scholar Position in Machine Learning

Princeton University's Cooperative Institute for Modeling the Earth System (CIMES) has an opening at the rank of Associate Research Scholar, to perform research in applying Machine Learning to Earth system modeling and analysis. We seek dynamic and exceptional early career scientists (at least 3, not more than 6 years' post-doctoral experience) to work at CIMES joining a new and exciting initiative in collaboration with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL).

The candidate is expected to build and lead an independent research effort applying machine learning toward one of NOAA's mission goals that is supported at CIMES: namely the Weather-Ready Nation goal. The goal broadly covers activities using models, observations, theory, and machine learning techniques to improve the quality of forecasts and predictions at subseasonal to interannual timescales. The current interest is in understanding the impact of ocean processes to prediction problems at this range of timescales, and using machine learning to optimize the application of observational constraints.

The current research effort is fully funded for two years. The position will be a two-year term position with possibility of renewal dependent on funding and performance. The candidate will be given every opportunity to build a new core activity centered on machine learning, in conjunction with GFDL scientists. We are seeking exceptional candidates who promise to bring creative and transformational approaches to Earth system modeling and the prediction enterprise.

Curriculum vitae a publication list, and contact information for three references, should be submitted online to https://www.princeton.edu/acad-positions/position/11882. Princeton University is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.  This position is subject to the University's background check policy.