HAI Postdoctoral Fellowship in the Remote Sensing Ecohydrology Group, Department of Earth System Science
The HAI Postdoctoral Fellow will lead a new project in the Remote Sensing Ecohydrology Group, led by Professor Alexandra Konings in the department of Earth System Science. This project aims to build a new hybrid AI – biogeophysical model to estimate the spatio-temporal dynamics of water flow through the soil and water uptake by plants. The approach will constrain a model of subsurface flow and vegetation water uptake with readily available global data of aboveground vegetation water content, leaf area index, and other variables to constrain patterns of belowground water uptake. Automatic differention-enabled deep learning methods will be used to ensure the satellite data are able to constrain the belowground inferences. Specific research questions addressed with the model could include but are not limited to: a) quantification of the relationship between microwave remote sensing derived proxies and vegetation hydrologic conditions across the globe, b) representation of those belowground components of the hydrologic cycle where classical understanding has been shown incorrect, and c) AI-inspired parameterization techniques to transfer the model’s findings across scales and regions.
Stanford HAI is also committed to creating a diverse community of scholars who are engaged in contributing to the understanding and advancement of Human-Centered AI. Postdoctoral fellows will have the opportunity to engage with one another and with the broader Stanford HAI research community. They are also expected to participate in professional development, cohort-building, and other programmatic activities organized by HAI.
- PhD in environmental science, environmental engineering, computer science electrical engineering, or a related field.
- Experience developing AI-based modeling in hydrology or ecology is required. Experience using hydrologic or microwave remote sensing data is a plus.
- The ideal candidate will also have experience using hybrid AI - physical modeling approaches and/or experience using AI for parameter inference.
- Completion of all doctoral requirements within the last three years and no later than September 1, 2023
Candidates will be evaluated based on having a strong record of peer-reviewed scientific publications, research expertise and experience, and strong oral and written communication skills.
- Application Deadline: March 20, 2023 (Applicants advancing in the review process may be asked to submit additional materials, including letters of recommendation, and may be invited to interview.)
- Selections to be made by mid-April, 2023
- This is a 1-year appointment starting Fall 2023
For full consideration, send a complete application in a single PDF to HAI-Fellowships@stanford.edu with the subject line: “HAI Postdoc Fellowship - Mineral X.”
Complete applications will include:
The expected base pay for this position is $80,000/yr. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the qualifications of the selected candidate, budget availability, and internal equity.
Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.