Rice University is inviting applications for a fully funded PhD position in Computational Hydrology and Remote Sensing, starting Fall 2026. The position is supervised by Assistant Professor Noemi Vergopolan and focuses on computational and data-intensive research at the intersection of water resources, artificial intelligence, and satellite remote sensing. Research topics include advancing terrestrial hydrology, physics-informed AI modeling for hydrologic prediction, satellite data assimilation, and studying droughts and agricultural resilience. Students in this group will work with large-scale datasets, advanced models, and AI tools, collaborating across Earth science, engineering, and data science disciplines. The research aims to address problems with direct societal and decision-making relevance. The PhD program is part of the Department of Earth, Environmental and Planetary Sciences (EEPS) at Rice University, which offers a multidisciplinary environment and collaborates with several research centers, including the Center for Computational Geophysics and the Ken Kennedy Institute for Information Technology. Applicants from diverse academic backgrounds such as geosciences, physics, chemistry, mathematics, engineering, or computer science are encouraged to apply. The program does not require GRE scores, and international students must provide proof of English proficiency unless their previous instruction was in English. The application package should include transcripts, three letters of recommendation, and a statement of research goals. The application deadline is January 9, 2026. All full-time PhD students receive a tuition waiver and a competitive stipend for the duration of their studies, funded through university endowment, fellowships, research grants, and teaching assistantships. Additional competitive fellowships may be available. For more information, visit the department website or contact the graduate program administrator. Key research areas: Computational Hydrology, Remote Sensing, Artificial Intelligence, Water Resources, Physics-informed AI, Hydrologic Prediction, Data Assimilation, Droughts, Agricultural Resilience, Earth Science. Application links and further details are provided in the announcement.