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Postdoctoral Associate

3 months ago

Ithaca, New York, United States EAS, Cornell University Full time

Academic Job Description

Department of Earth and Atmospheric Sciences

Postdoctoral Associate

The College of Agriculture and Life Sciences (CALS) is a pioneer of purpose-driven science and Cornell University's second largest college. We work across disciplines to tackle the challenges of our time through world-renowned research, education, and outreach. The questions we probe and the answers we seek focus on three overlapping concerns: We believe that achieving next-generation scientific breakthroughs requires an understanding of the world's complex, interlocking systems. We believe that access to nutritious food and a healthy environment is a fundamental human right. We believe that ensuring a prosperous global future depends on the ability to support local people and communities everywhere. By working in and across multiple scientific areas, CALS can address challenges and opportunities of the greatest relevance, here in New York, across the nation, and around the world.

Position Function:

The Department of Earth and Atmospheric Sciences at Cornell University is seeking a Postdoctoral Associate to develop cutting-edge machine learning and AI techniques for improving drought prediction, seasonal forecasting, and numerical weather and climate modeling. Climate change is exacerbating the occurrence and intensity of extreme events, such as droughts, and there is an urgent need for advanced computational methods to enhance our understanding and prediction capabilities of these phenomena. This position is supported outside of traditional funding mechanisms, and hence the Postdoctoral Associate will have additional flexibility in learning, adapting, and publishing on ML/AI and its applications to atmospheric and climate science research.

The primary focus of this position is to leverage state-of-the-art machine learning and AI algorithms to extract valuable insights from high-resolution climate datasets, including dynamical, classical statistical, and machine learning-based downscaling. The overarching goal is to improve the usability of climate information for practitioners by better quantifying and communicating uncertainties in regional and local climate projections, with a particular emphasis on extreme events.

This project offers an exciting opportunity to bridge fundamental and applied climate science, allowing you to develop a diverse skill set desirable in both the private and public sectors. You will have the chance to collaborate with renowned institutions such as the National Center for Atmospheric Research (NCAR) and the National Oceanic and Atmospheric Administration's Geophysical Fluid Dynamics Laboratory (NOAA GFDL), as well as publish research in top-tier journals.

This position is full-time and will be located in Ithaca, New York.

This is a one-year appointment with a possible extension depending on funding and performance. Anticipated Division of Time


• Develop, design, and apply Machine Learning/AI algorithms and workflows for improving weather, seasonal forecasts, and climate model projections. Applications might include (but are not limited to) novel downscaling methods, ensemble post-processing and bias correction, model parameterization, land-atmosphere coupling analysis, and new experimental approaches for synthesizing observational data with model output (40%).

• Write manuscripts and publish results in high-quality, peer-reviewed journals, and present results at conferences and seminars (40%).

• Self-directed research and professional development (10%).

• Attend conferences and travel to collaborate with project partners (5%).

• Mentorship and knowledge sharing: activities could include helping to organize dept. hackathons, assisting graduate and undergraduate students with research projects, guest lectures, etc. (5%).

Requirements


• Ph.D. in atmospheric science or related field.

• Experience with climate models, seasonal forecast products (e.g., NMME), numerical weather models, or expertise in other aspects of working with large geospatial datasets.

• Excellent written and oral communication skills.

• Proficiency in Python (preferred) or other data analysis software.

• Ability and desire to pursue research both independently and as part of a team.

• Evidence of interest in teaching, mentoring, or outreach.

• Must be able to meet the travel requirements of the position, and have reliable transportation as well as have and maintain a valid and unrestricted New York State driver's license and be cleared to drive for university business.

Supervision Exercised The successful candidate is expected to work with undergraduate and graduate group members on related or different research and provide mentorship and guidance when collaborating.