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 firstname.lastname@example.org
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.