Fully Funded PhD in Hydrologic Modeling and Machine Learning at Utah State University Utah State University in United States

The Department of Civil and Environmental Engineering at Utah State University is advertising a fully funded PhD opportunity in the Shuai Computational and Integrated Hydrology Group led by Pin Shuai.

The project focuses on hydrologic modeling and machine learning, with an emphasis on predicting baseflow in snow-dominated watersheds using a combination of physics-based and data-driven models. This is a strong fit for students interested in computational hydrology, watershed processes, groundwater-surface water interactions, and environmental modeling.

Funding: The position is described as a fully funded PhD research assistantship.

Eligibility highlights: Applicants should have an M.S. or B.S. in Civil and Environmental Engineering or a closely related field such as hydrology, geoscience, or environmental science. Strong quantitative skills are required. Prior experience with hydrologic modeling and/or machine learning is preferred, along with programming ability in Python or another language. Strong written and verbal communication skills are also expected.

How to apply: Interested candidates should email Dr. Pin Shuai at [email protected]. A link with additional details is included in the post.

Application window: The position remains open until filled.

Research keywords: hydrologic modeling, machine learning, groundwater hydrology, snow hydrology, baseflow prediction, computational hydrology, civil and environmental engineering.