[ES_JOBS_NET] Associate Research Scientist, Columbia Climate School

[Christine Wiedinmyer]

https://academic.careers.columbia.edu/#!/189740

Associate Research Scientist
#189740
Description

The Center for Climate Systems Research (CCSR) in the Columbia Climate
School invites applications for an Associate Research Scientist (ARS) in
cloud microphysics modeling. We seek a highly motivated scientist with a
strong background in atmospheric science and a demonstrated record of
research contributions to cloud and precipitation processes. This position
is closely aligned with CCSR’s strategic focus on Earth System
Intelligence: using AI-driven climate modeling, NASA satellite
observations, and Earth system models to reduce uncertainty in the
representation of key climate processes.

Warm clouds play a central role in Earth’s radiation budget and water
cycle, yet they remain poorly understood and challenging to
simulate. Clouds are also a central source of uncertainty in climate
projections, making improved cloud microphysics essential to CCSR’s broader
goal of advancing next-generation models of clouds and atmospheric
processes, from process-scale understanding to global Earth system
applications. A key reason is that cloud and precipitation processes span a
vast range of scales, from the microphysics of individual particles to the
planetary-scale dynamics of Earth’s general circulation. The ARS will
address two fundamental challenges that arise from this complexity: how to
tractably represent the population statistics of cloud and precipitation
particles in models and how microphysical processes couple to macroscale
cloud properties and evolution, including precipitation initiation.

The successful candidate will develop and evaluate microphysics
parameterizations using a hierarchy of model frameworks, from
high-resolution process models to global ESM simulations, guided by
observational data from field campaigns and satellite platforms. This work
will connect directly to CCSR’s dual advantage at the intersection of
NASA’s Goddard Institute for Space Studies and the Columbia Climate School
by integrating space-based Earth observations, process-level modeling, the
NASA GISS ModelE Earth system model, and real-world validation. Among other
methods, the ARS will apply machine learning and Bayesian statistics to
develop improved parameterizations and systematically distinguish
structural from parametric uncertainty in model representations of cloud
processes.

The ARS will collaborate with partners at NASA GISS and contribute to
CCSR’s mission to advance climate modeling and actionable Earth system
understanding.

Responsibilities

   - Design and execute research using a hierarchy of
   atmospheric and microphysical models to evaluate and improve warm cloud
   microphysics parameterizations across scales.
   - Use remote sensing, field campaign, and satellite observations to
   constrain and evaluate model structural assumptions.
   - Contribute to CCSR’s Earth System Intelligence research area by
   improving the representation of cloud and precipitation processes in
   next-generation climate and Earth system models, including connections to
   NASA satellite observations and the NASA GISS ModelE framework.
   - Publish research findings in peer-reviewed journals and present at
   national and international scientific conferences.
   - Contribute to the preparation of competitive grant proposals to
   federal funding agencies and applications for HPC allocations.

Qualifications

Minimum Qualifications

   - Ph.D. in Atmospheric Science, Earth Science, Applied Mathematics,
   Physics, or a closely related field.
   - Proficiency in Fortran and at least one scientific analysis language,
   e.g., Python, Julia, MATLAB.
   - Demonstrated experience with numerical modeling.

Preferred Qualifications

   - Postdoctoral research experience in atmospheric or Earth science.
   - Experience with atmospheric models such as Large Eddy Simulations
   (LES) and/or Earth System Models (ESMs).
   - Experience modeling microphysical processes, especially with
   Lagrangian, particle-based approaches, and/or experience working with cloud
   observational data, especially radar spectra.
   - Experience running and analyzing simulations on high-performance
   computing (HPC) platforms.
   - Familiarity with Bayesian statistical methods or parameter estimation
   applied to complex models.
   - Experience with scientific machine learning.
   - A track record of contributing to successful federal grant proposals.
   - Interest in contributing to CCSR’s broader research mission at the
   intersection of AI-driven climate modeling, NASA Earth
   observations, and Earth system model development.

The search will remain open for at least 30 days after the ad appears and
will continue until the position is filled.

Appointments are made on a fiscal year basis and are eligible for renewal
each July 1, contingent upon performance and funding.



Columbia University benefits accompany this Officer of Research appointment.
Application Instructions

We accept online applications only
Equal Employment Opportunity Statement

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.
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