Associate Research Scientist | Columbia University - Military Veterans

Columbia Engineering, the Fu Foundation School of Engineering and Applied Science at Columbia University in the City of New York invites applications for an Associate Research Scientist in the field of global atmospheric modeling and machine learning, under the supervision of Greg Elsaesser at Columbia University/NASA GISS and Brian Medeiros at NSF NCAR (National Center for Atmospheric Research). The position is part of the National Science Foundation-funded Learning the Earth with Artificial intelligence and Physics (LEAP) Science and Technology Center (STC), https://leap.columbia.edu/ , a multi-institutional center effort meant to improve climate projections using novel artificial intelligence for better climate adaptation. The goal of this project is to build key connections that enable results from machine learning activities developed within LEAP to be incorporated and evaluated in the atmospheric component of the Community Earth System Model (CESM). This work is expected to proceed in two parallel efforts that will be coordinated by the incumbent. The first builds on ongoing work and focuses on conducting and analyzing perturbed physics ensembles with the Community Atmosphere Model (CAM) to quantify sensitivity of the simulated climate to parameter choices. Machine learning approaches will be applied to provide actionable information about parameter sensitivity and optimization for specific climatic targets. By applying parameter estimation techniques within the development version of CAM, this project will inform model development in real time. This ARS will work with LEAP and NCAR scientists to build the workflows that allow for rapid production, analysis, and emulation of PPEs and to disseminate findings to CESM developers and the wider research community. The second, equally important, aim of the project is to establish more general support and coordination of LEAP-developed machine learning activities, including conducting and analyzing experiments using ML-based parameterizations and emulators as well as explorations of methods to generate high-quality training data sets for additional ML-based schemes. The ARS will closely collaborate with members of the Atmospheric Modeling and Predictability Section in the Climate and Global Dynamics Laboratory at NCAR as well as with graduate students, postdocs, and other staff within LEAP. The applicant should have a background in atmospheric modeling, atmospheric science, or closely related fields, and ideally should have significant experience in machine learning or statistics. One of LEAP's goals is to increase the diversity in climate science and data science. We welcome and encourage applications from individuals of all backgrounds and identities. We are committed to building a diverse and inclusive community and believe that a variety of perspectives and experiences is essential to advancing our research and mission. Minimum Qualifications : A Ph.D. in Atmospheric Science, Data Science, Computer Science, Physics, Earth System Science or a directly related discipline is required by the start of the appointment. Strong programming skills are a requirement.= Preferred Qualifications : Post-doctoral experience and demonstrated experience in Earth System Science, Data Science, or similar. Fluency in Python. Familiarity with fortran. Advanced experience in machine learning. Demonstrated experience in statistical/mathematical analyses of model output and/or observational datasets. Experience running and analyzing global climate simulations on high performance computing platforms Excellent command of the English language (verbal and written) and strong communication skills are desired. 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. 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.