The Computational Hydrology and Remote Sensing research group at Rice University focuses on the intersection between water resources, food, and climate. Our research aims to aid actionable decision-making by improving hydrological information for monitoring and forecasting hydrological extremes and their impacts at the local scales. To this end, we develop scalable computational approaches for hyper-resolution hydrological prediction targeting regional to global scale applications by leveraging advances in satellite remote sensing, land surface modeling, artificial intelligence, big geospatial data, data fusion, and high-performance computing. More information at www.waterai.earth.

Our research group works on multidisciplinary projects in the following areas (and at their intersections) : 

• Advancing terrestrial hydrology through numerical modeling and physics-based AI
• Satellite remote sensing and AI land data assimilation
• Integrated Earth system modeling and climate change impacts
• Numerical prediction of extreme hydroclimate events (floods, droughts, wildfires)
• Quantitative assessments of climate change and human interventions on water scarcity

PhD position on Soil Moisture Remote Sensing and Modeling

We have one position available to develop high-resolution estimates of surface and root-zone soil moisture by fusing microwave, thermal, and synthetic-aperture radar satellite remote sensing data with numerical hyper-resolution land surface models using AI-driven land data assimilation techniques. The student will explore how local-scale soil moisture dynamics influence droughts, providing critical insights into managing water resources under changing climate conditions.

Background:

Successful candidates will have a background in applied math, engineering, geosciences, environmental and climate sciences, physics, scientific deep learning, or related fields. This is an excellent opportunity to develop expertise in numerical hydrologic prediction, satellite remote sensing (e.g., NASA, NOAA, ESA, Planet), and data science while contributing to cutting-edge research in computational Earth science.

Essential Qualifications: 

• Strong analytical skills, ability to think critically and solve problems effectively.
• A solid background in calculus and numerical methods.
• Strong programming skills (UNIX/Linux, Python, shell scripting) for analysis and visualization of model simulations and remote sensing data.
• Experience with geospatial datasets (e.g., GeoTIFF, NetCDF, HDF5, Zar, dask) and data processing libraries (e.g., xarray, cartopy, rasterio).
• Familiarity with deep learning concepts and workflows using Python (e.g., PyTorch, TensorFlow)
• Excellent written and verbal communication.

Preferred Qualifications:

• Demonstrate a research track record of involvement in topics relevant to computational hydrology, remote sensing, or related fields.
• Experience with an advanced programming language (e.g., C, C++, Fortran, etc.)
• Hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Experience with High-performance computing (HPC) systems and/or cloud computing (e.g., AWS, Google Cloud, etc.).
• Familiarity with version control systems for collaborative code development (e.g., Github, Gitlab)

All researchers will benefit from our group's involvement with national and international collaborative projects, as well as Rice's thriving and expanding programs, such as the Rice Data Science Initiative, Data to Knowledge Lab, Ken Kennedy Insitute, and the Rice Space Institute.

We believe that a diverse team enriches our workplace and enhances our impact. We strongly encourage applications from women, individuals from underrepresented minority groups, and all who can contribute to the further diversification of ideas and perspectives. 

Application: 

PhD students should submit an application to Earth, Environmental, and Planetary Sciences (deadline of January 3rd, 2025). International students should also meet the language proficiency requirements. Prospective graduate students can email Dr. Vergopolan (Noemi.Vergopolan@rice.edu) with the subject "Prospective PhD student" before applying. In the email, please include the following items: unofficial transcripts, curriculum vitae, names and contact information of three references, and a brief personal statement of why they would like to join the group. We greatly appreciate all the applications, but given the high volume of submissions, please note that only candidates shortlisted for interviews will be notified. Depending on funding availability, we can accept students in both Fall and Spring semesters. Therefore, the enrollment time is flexible.