Postdoc position in remote sensing, water quality, and machine learning – Ohio State University

The Ohio State University
Columbus, Ohio
Job Category
Post Doctoral Appointments
TBD and negotiable
Start Date
Last Date to Apply
School of Environment and Natural Resources at the Ohio State University is seeking a postdoc fellow to work with Drs. Kaiguang Zhao, Sami Khanal, and Alexis Londo in the areas of satellite time-series analysis, water quality remote sensing, and machine learning. The postdoc will contribute to research activities of a state-funded project on synthesis of satellite images and in-situ water quality measurements to predict and monitor harmful algae blooms in inland lakes. Potential research topics include exploring long-term Landsat and Sentinel multispectral imagery to understand historical patterns and drivers of inland water quality dynamics, building remote sensing-based machine learning models to estimate water quality parameters (e.g., chlorophyll-a, phycocyanin, and Microcystin), developing and testing new satellite time-series algorithms for trend analysis and change detection, and building a remote sensing-based framework to monitor harmful algae blooms. The postdoc also has the flexibility to develop his/her own research questions within the generic scope of water quality, remote sensing, or machine learning. Primary duties for this position include: • Conduct research activities on predicting and monitoring inland harmful algae blooms (50%) • Compile, manage, and analyze satellite imagery • Help to supervise a Master graduate research assistant on the project • Contribute to first-authored or co-authored scientific publications • Present outcomes of the project at scientific conferences and meetings This is a one-year position but is extendable if new funding becomes available. The position starts immediately and will be open till filled. Applications should include a Curriculum Vitae and a cover letter briefly stating backgrounds and experiences relevant to the position. Informal inquiries are also encouraged and can be directed to
The ideal candidate should have the following qualifications and experiences: • A PhD degree or equivalent in geography, remote sensing, computer sciences, environmental sciences, ecology, Earth science, geosciences, or related fields • Demonstrated experiences in statistical modeling, data analytics, machine learning, or deep learning • Familiarity with satellite image analysis and GIS; proficiency in programming (e.g., C/C++, Matlab, Python or R). • Skills and knowledge in watershed modeling (e.g., SWAT) desirable but not essential.
Contact Person
Kaiguang Zhao
Contact eMail
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