Professional Specialist in Climate Prediction
The Atmospheric and Oceanic Sciences Program at Princeton University in cooperation with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) seeks a motivated individual to join a team conducting experimental real-time seasonal to interannual climate prediction and related research. The incumbent will be part of a team that is responsible for gathering observations and running real-time experimental seasonal to interannual predictions using state of the art climate models. The incumbent will (a) perform climate model simulations, (b) obtain observational data needed for the predictions and perform quality control on that data, and (c) conduct directed research towards improving our understanding of seasonal to decadal climate variability and predictability, as well as towards improving our prediction system.
The selected candidate will have a graduate degree in meteorology, oceanography, or in a related field, and will possess one or more of the following attributes: (a) strong background in climate dynamics, (b) strong computational skills, including experience using coupled climate models and (c) strong diagnostic skills in analyzing simulated and observed data sets. Appointments are initially for one year with renewal contingent upon satisfactory performance and continued funding.
Successful candidates will be based at the Geophysical Fluid Dynamics Laboratory (GFDL) in Princeton, New Jersey. For further information, please contact Tom Delworth (Tom.Delworth@noaa.gov) or Nat Johnson (Nathaniel.Johnson@noaa.gov). Complete applications, including a CV, three references in order to solicit letters of recommendation, and a one-to-two page statement of professional interests must be submitted to https://www.princeton.edu/acad-positions/position/9461by December 15, 2018 to ensure full consideration. Review of applications will begin as soon as they are received, and continue until the position is filled. This position is subject to the University's background check policy.