[ES_JOBS_NET] Postdoc in Machine Learning of Earth System Model Data at Columbia University

[Gregory Elsaesser]

Columbia University/NASA-GISS, Postdoctoral Research Scientist in Machine
Learning of Earth System Model Data

The Department of Applied Physics and Applied Mathematics (APAM) at
Columbia University invites applicants for a Postdoctoral Research
Scientist position in machine learning and climate science at its
Morningside campus in New York City.

The Postdoctoral Research Scientist will focus on using machine learning
(ML) to develop improved and flexible ML emulators of diverse Earth System
Model (ESM) inputs and outputs. The emulators will serve as
inexpensive-to-run, accurate surrogates for ESM components. Bayesian
methods will be applied to the emulators (with outputs scored against
existing aggregated observations) so as to efficiently optimize ESM
parameter settings, thus improving ESMs and enabling more skillful climate
projections. The emulators and parameter inference code will be trained and
tested on output from NASA-GISS ModelE (atmosphere) as well as output from
models at the National Center for Atmospheric Research (NCAR; both from the
CESM atmosphere [CAM] and land model [CLM]). The Postdoctoral Research
Scientist will work closely with Dr. Gregory Elsaesser (APAM & NASA GISS),
Dr. Marcus van Lier-Walqui (Center for Climate Systems Research [CCSR] &
NASA GISS), and a supportive team that includes machine learning experts
and atmospheric scientists at both the National Center for Atmospheric
Research (NCAR) and the Center for Learning the Earth with Artificial
Intelligence and Physics (or LEAP, an NSF Science and Technology Center
(STC) at Columbia University launched in 2021; https://leap.columbia.edu).

The Postdoctoral Research Scientist will be expected to organize and chair
a LEAP research working group related to ML-parameter inference in models,
lead peer-reviewed publications, and to present their results at LEAP and
other scientific meetings. The Postdoctoral Research Scientist should also
expect to occasionally (though infrequently) travel to NCAR as part of
multi-institutional collaborations.
Minimum Qualifications-A Ph.D. in Data Science, Bayesian Statistics,
Computer Science, Physics, Earth System Science, Atmospheric Science, 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.-Excellent command of the
English language (verbal and written) and strong communication skills are
desired.

Application Information:

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

We accept online applications only.

To apply to this position, please go to: https://apply.interfolio.com/103545

Please also feel free to reach out to Gregory Elsaesser (
gregory.elsaesser at columbia.edu) with any questions.
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Gregory Elsaesser
NASA Goddard Institute for Space Studies
Columbia University, Dept. of Applied Physics and Applied Mathematics
2880 Broadway, Room 632
New York, New York 10025
Office: 212-678-5596
Email: gregory.elsaesser at columbia.edu (preferred)
Email: gregory.s.elsaesser at nasa.gov (secondary)
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