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Associate Research Scientist

4 months ago

New York, New York, United States Columbia University Full time

Columbia Engineering, the Fu Foundation School of Engineering and Applied Science at Columbia University in the City of New York invites applications for an Associate Research Scientist in the field of land modeling and machine learning, under the supervision of Pierre Gentine at Columbia University and David Lawrence at the National Center for Atmospheric Research (NCAR). The position is part of the National Science Foundation-funded Learning the Earth with Artificial intelligence and Physics (LEAP) Science and Technology Center (STC), , a multi-institutional center effort meant to improve climate projections using novel artificial intelligence for better climate adaptation. The position will be based at NCAR in Boulder, Colorado.

The aim of this project is to develop an open-source process for systematic parameter estimation for the Community Land Model (CLM), drawing on domain expertise from CLM scientists and machine learning emulation and optimization methodologies. Full complexity land models like CLM include a large number of parameters that influence the biophysical and biogeochemical processes that determine fluxes and states predicted by the model. Prior studies have demonstrated that important emergent properties of the land system (e.g., CO2 fertilization of plants or runoff response to temperature or precipitation perturbations) exhibit strong parametric uncertainty. A successful methodology to estimate parameter values and uncertainty in these parameter values has promise to reduce uncertainty in Earth System model simulations of terrestrial carbon, water, and energy responses to and impacts on climate change. A second, equally important, aim of this project is to establish more general support and coordination of land-oriented LEAP ML-based parameterization development and parameter optimization activities. This will include conducting and analyzing CLM experiments using new ML-based parameterizations and optimal parameter settings and contributing to the integration and testing of these developments into the broader CESM effort.

The ARS will closely collaborate with members of the Terrestrial Sciences Section in the Climate and Global Dynamics Lab at NCAR as well as with graduate students, postdocs, and other staff within LEAP.

The applicant should have a background in land modeling and terrestrial science, and ideally should have advanced experience in machine learning or statistics.

One of LEAP's goals is to increase the diversity in climate science and data science. We welcome and encourage applications from individuals of all backgrounds and identities. We are committed to building a diverse and inclusive community and believe that a variety of perspectives and experiences is essential to advancing our research and mission.


Minimum Qualifications
  • Strong programming skills are a requirement.
Preferred Qualifications
  • A Ph.D. in Data Science, Computer Science, Physics, Earth System Science or a directly related discipline, or equivalent experience.
  • Fluency in Python.
  • Advanced experience in machine learning.
  • Demonstrated experience with large-scale models.
  • Excellent command of the English language (verbal and written) and strong communication skills are desired.

Applications must include: (a) curriculum vitae (b) statement of research (optional) (c) names of at least three references who may be asked to provide letters.

Columbia University is an Equal Opportunity Employer / Disability / Veteran

Pay Transparency Disclosure

The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.