Computational Scientist position on Python-based ML-enabled weather and climate modeling Princeton University
The Atmospheric and Oceanic Sciences Program at Princeton University, in cooperation with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a professional specialist/computational scientist to integrate a computationally-advanced Python-based machine-learning (ML) augmented atmosphere model into the existing GFDL modeling system. The goal is to improve predictions of extreme precipitation events by permitting ML-powered model improvements. The successful applicant is expected to work with Lucas Harris at GFDL, Chris Bretherton and Oliver Fuhrer at the Allen Institute for Artificial Intelligence (AI2), as well with other scientists in GFDL's Weather and Climate Dynamics Division and AI2. This candidate would port AI2's Python-wrapped climate model workflow into GFDL's System for High-resolution prediction on Earth-to-Local Domains (SHiELD) and set up the AI2 machine-learning workflow on a NOAA modeling system. The candidate would work to update the model with the newest version of SHiELD's codes and continue to integrate new updates to SHiELD and in AI2's ML workflow. This candidate would then apply the ML-augmented model to create accurate and inexpensive large ensembles to improve predictions of extreme precipitation events on weekly to subseasonal timescales. Scientists or engineers with a strong background in software engineering, computer science, computational science, high-performance computing, or machine learning are encouraged to apply. Knowledge of both Python and Fortran would be extremely useful, as would some experience with hydrodynamic codes and machine learning software. This is a one-year position with potential for renewal based on candidate performance and continued funding. Candidates must have at least a Master's degree in an appropriate field. Complete applications include a CV, publication list, and 3 letters of recommendation. Review of applications will begin September 1, 2022 and will continue until the position is filled. Princeton is interested in candidates who, through their research, will contribute to the diversity and excellence of the academic community. Applicants should apply online at https://www.princeton.edu/acad-positions/position/26786. For more information about the research project and application process, please contact Lucas Harris at firstname.lastname@example.org. This position is subject to the University's background check policy. Princeton University is an equal opportunity/affirmative action employer, and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.
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