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<p class="MsoNormal">Hello,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Below I have included an Associate Research Scientist position located within the Climate School .<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b>Position Description</b><o:p></o:p></p>
<p class="MsoNormal">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.<o:p></o:p></p>
<p class="MsoNormal">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.<o:p></o:p></p>
<p class="MsoNormal">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.<o:p></o:p></p>
<p class="MsoNormal">The ARS will collaborate with partners at NASA GISS and contribute to CCSR’s mission to advance climate modeling and actionable Earth system understanding.<o:p></o:p></p>
<p class="MsoNormal">Responsibilities<o:p></o:p></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="mso-list:l0 level1 lfo1">Design and execute research using a hierarchy of atmospheric and microphysical models to evaluate and improve warm cloud microphysics parameterizations across scales.<o:p></o:p></li><li class="MsoNormal" style="mso-list:l0 level1 lfo1">Use remote sensing, field campaign, and satellite observations to constrain and evaluate model structural assumptions.<o:p></o:p></li><li class="MsoNormal" style="mso-list:l0 level1 lfo1">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.<o:p></o:p></li><li class="MsoNormal" style="mso-list:l0 level1 lfo1">Publish research findings in peer-reviewed journals and present at national and international scientific conferences.<o:p></o:p></li><li class="MsoNormal" style="mso-list:l0 level1 lfo1">Contribute to the preparation of competitive grant proposals to federal funding agencies and applications for HPC allocations.<o:p></o:p></li></ul>
<p class="MsoNormal"><b>Qualifications</b><o:p></o:p></p>
<p class="MsoNormal">Minimum Qualifications<o:p></o:p></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="mso-list:l2 level1 lfo2">Ph.D. in Atmospheric Science, Earth Science, Applied Mathematics, Physics, or a closely related field.<o:p></o:p></li><li class="MsoNormal" style="mso-list:l2 level1 lfo2">Proficiency in Fortran and at least one scientific analysis language, e.g., Python, Julia, MATLAB.<o:p></o:p></li><li class="MsoNormal" style="mso-list:l2 level1 lfo2">Demonstrated experience with numerical modeling.<o:p></o:p></li></ul>
<p class="MsoNormal">Preferred Qualifications<o:p></o:p></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="mso-list:l1 level1 lfo3">Postdoctoral research experience in atmospheric or Earth science.<o:p></o:p></li><li class="MsoNormal" style="mso-list:l1 level1 lfo3">Experience with atmospheric models such as Large Eddy Simulations (LES) and/or Earth System Models (ESMs).<o:p></o:p></li><li class="MsoNormal" style="mso-list:l1 level1 lfo3">Experience modeling microphysical processes, especially with Lagrangian, particle-based approaches, and/or experience working with cloud observational data, especially radar spectra.<o:p></o:p></li><li class="MsoNormal" style="mso-list:l1 level1 lfo3">Experience running and analyzing simulations on high-performance computing (HPC) platforms.<o:p></o:p></li><li class="MsoNormal" style="mso-list:l1 level1 lfo3">Familiarity with Bayesian statistical methods or parameter estimation applied to complex models.<o:p></o:p></li><li class="MsoNormal" style="mso-list:l1 level1 lfo3">Experience with scientific machine learning.<o:p></o:p></li><li class="MsoNormal" style="mso-list:l1 level1 lfo3">A track record of contributing to successful federal grant proposals.<o:p></o:p></li><li class="MsoNormal" style="mso-list:l1 level1 lfo3">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.<o:p></o:p></li></ul>
<p class="MsoNormal">The search will remain open for at least 30 days after the ad appears and will continue until the position is filled.<o:p></o:p></p>
<p class="MsoNormal">Appointments are made on a fiscal year basis and are eligible for renewal each July 1, contingent upon performance and funding.<o:p></o:p></p>
<p class="MsoNormal"> <o:p></o:p></p>
<p class="MsoNormal">Columbia University benefits accompany this Officer of Research appointment.<o:p></o:p></p>
<p class="MsoNormal"><b>Application Instructions</b><o:p></o:p></p>
<p class="MsoNormal">We accept online applications only <o:p></o:p></p>
<p class="MsoNormal"><b>Equal Employment Opportunity Statement</b><o:p></o:p></p>
<p class="MsoNormal">Columbia University is an Equal Opportunity Employer / Disability / Veteran<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><a href="https://apply.interfolio.com/191580">https://apply.interfolio.com/191580</a><o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><b>Saul Vazquez-Pichardo</b><o:p></o:p></p>
<p class="MsoNormal">Talent & Inclusion Manager | Climate School, Columbia University<o:p></o:p></p>
<p class="MsoNormal">Email: <a href="mailto:svazquezpichardo@climate.columbia.edu">svazquezpichardo@climate.columbia.edu</a> Cell:646-812-8443<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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