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Columbia University Postdoctoral Research Scientist in New York, New York

The Department of Earth and Environmental Engineering of Columbia University in the City of New York and the National Center for Atmospheric Research (NCAR) invites applications for a Postdoctoral Position in the field of land model parameter estimation, using machine learning, under the supervision of Dr. David Lawrence at NCAR and Prof. Pierre Gentine at Columbia University. The position is part of the recent National Science Foundation Learning the Earth with Artificial intelligence and Physics (LEAP) Science and Technology Center (STC),, a large multi-institutional center effort meant to improve climate projections using novel artificial intelligence for better climate adaptation.

The aim of this project is to develop an open-source process for systematic parameter estimation for the Community Land Model (CLM), which is the land component of the Community Earth System Model (CESM), drawing on domain expertise from CLM scientists and machine learning emulation and optimization methodologies. Full complexity land models like CLM include a massive number of parameters that influence the biophysical and biogeochemical processes that determine the fluxes and states predicted by the model. The scope of the problem is large. More than 200 parameters have been identified within CLM, with many of these parameters also varying by Plant Functional Type (PFT). 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

The postdoctoral research scientist will collaborate with a team of scientists at NCAR including David Lawrence, Katie Dagon, and Daniel Kennedy as well as Pierre Gentine and other CLM collaborators working on the parameter estimation problem in CLM as well as with a Working Group on ML-based Parameter Inference within LEAP. Though the postdoctoral research scientist will be hired as a Columbia University employee, the place of work is expected to be at NCAR in Boulder, CO, working within the Terrestrial Sciences Section in the NCAR Climate and Global Dynamics Laboratory. Flexible work arrangements may be possible.

The applicant should ideally have experience in machine learning or statistics and/or have a strong background in terrestrial systems modeling. Candidates should have recently completed their Ph.D. or should expect to complete their degree requirements by summer 2022.

One of LEAP’s goals is to increase the diversity in geosciences and data science, and to be on par with the US population by the end of the Center lifetime. The Center will take proactive steps to reach its diversity goal. We thus particularly welcome applicants from underrepresented groups. Our group also strives to improve and ensure work-life balance for their employees.

Minimum Qualifications

  • A Ph.D. in Data Science, Bayesian Statistics, Computer Science, Physics, Earth System Science, Ecology, Hydrology, or a directly related discipline is required by the start of the appointment.

  • Strong programming skills are a requirement.

Preferred Qualifications

  • Fluency in Python.

  • Advanced experience in machine learning.

  • Demonstrated experience in statistical/mathematical analyses of model output and/or observational datasets.

  • Demonstrated experience running, modifying, and analyzing large-scale land, hydrology, or ecology or equivalent models.

  • Excellent command of the English language (verbal and written) and strong communication skills are desired.

Columbia University is an Equal Opportunity Employer / Disability / Veteran