Associate Research Scientist

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.

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.

instructions

We accept online applications only

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.