Postdoctoral Associate in Water Resources Systems Modeling Under Deep Uncertainty
School of Civil and Environmental Engineering
Professor Patrick Reed’s Decision Analytics for Complex Systems research group within the School of Civil and Environmental Engineering at Cornell University seeks one or more postdoctoral associates. The positions can be remote (but the home location needs to be in the United States). The position(s) will initially be available for one year with a potential renewal for two or more years.
Candidates should have strong computational skills, ideally with a focus on water resources systems modeling at the river-basin scale. Experience with high-performance computing, uncertainty analysis, and coupled human-natural systems modeling is desirable. Additionally, emerging advancements in control and machine learning (e.g., deep reinforcement learning) are of interest but not required. Ideally, candidates would have a Ph.D. in water resources engineering, hydrology, or a related field. The focus of this research will be on the risks, resilience, and sustainability of major regional water resources systems (e.g., the Mid-Atlantic, the Colorado River Basin, and the Central Valley). The research will have a particular emphasis on understanding the implications of human responses to evolving climate extremes.
To apply: Application materials must be submitted on-line through AcademicJobsOnline at https://academicjobsonline.org/ajo/jobs/24936
Through this website, applicants are to submit a curriculum vitae, graduate transcript, and the names and contact information for at least three references. The successful applicant anticipated hire date is September 1, 2023 (or earlier based on candidate’s availability). Review of applications will begin immediately and will continue until the position is filled. Questions can be directed to firstname.lastname@example.org
The School of Civil and Environmental Engineering at Cornell embraces diversity and seeks candidates who will contribute to a climate that supports students, faculty and staff of all identities and backgrounds. We strongly encourage individuals from underrepresented identities to apply.
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