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Computational Scientist position on Python-based ML-enabled weather and climate modeling
Princeton, New Jersey (US)
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The Atmospheric and Oceanic Sciences Program at Princeton University, in cooperation with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a computational scientist/scientific programmer to assist our scientists working to integrate a computationally-advanced Python-based machine-learning (ML) augmented atmosphere model into the existing GFDL modeling system. The goal is to permit ML-powered improvements to our models and to incorporate new diagnostics and use cases for this system. This project is funded by the NOAA Technology Incubator of the National Oceanic and Atmospheric Administration's Office of the Chief Information Officer.
The successful applicant will work with Lucas Harris at GFDL and Chris Bretherton and Oliver Fuhrer at the Allen Institute for Artificial Intelligence (AI2), and will work closely with other scientists in GFDL's Weather and Climate Dynamics Division and at AI2. This computational scientist will 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 computing system. The applicant will then 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 applicant will then assist GFDL and AI2 scientists in applying the ML-augmented model to scientific problems of interest.
Scientists or engineers with a strong background in software engineering, computer science, computational science, high-performance computing, machine learning, mathematics, or physics are encouraged to apply. Knowledge of both Python and Fortran will be extremely useful, as will some experience with hydrodynamic codes or numerical models, and machine learning software. Applicants should have strong experience working in a collaborative environment and an ability to learn new technologies. This is a one-year position with potential for renewal based on candidate performance and continued funding. Candidates should have at least a Master's degree in an appropriate field, including but not limited to computer science, mathematics, physics, atmospheric science, or engineering. Complete applications include a CV, publication list, and 2 letters of recommendation. Review of applications will begin immediately and 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/28201.
For more information about the research project and application process, please contact Lucas Harris at lucas.harris@noaa.gov. The position is subject to the University's background check policy.
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