Publisher
source

North Carolina State University

Fully Funded PhD in AI-Enabled Modeling of Agricultural Nitrogen Cycling and Greenhouse Gas Emissions North Carolina State University in United States

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded PhD position. The post states the student will be fully funded; no stipend amount, tuition details, or additional benefits are specified.

Deadline

Expired

Country flag

Country

United States

University

North Carolina State University

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Keywords

Computer Science
Machine Learning
Environmental Science
Agriculture
Biology
Soil Science
Earth Science
Uncertainty Analysis
Ecological Modeling
Nitrogen Cycling
Statistics
Greenhouse Gas

About this position

North Carolina State University is advertising a fully funded PhD position in the Soil Science Program focused on AI-enabled modeling of agricultural nitrogen cycling and greenhouse gas emissions. The project is part of a multi-institutional collaboration aimed at improving predictions of nitrogen use efficiency, N2O emissions, and nitrogen losses in agricultural systems.

The research emphasis includes process-based/biogeochemical models, AI-enabled agroecosystem modeling, model-data fusion, uncertainty quantification, and integration of high-frequency field measurements, soil microbiome data, and environmental datasets. This is a strong fit for students interested in environmental science, agriculture, soil science, earth system science, computer science, and statistics.

Desired preparation includes programming experience in Python, R, Julia, Fortran, C++, or similar languages, plus experience with ML/AI frameworks, statistical inference, and ecosystem/biogeochemical modeling. The post says applicants should have a BS or MS in earth system science, environmental/soil science, data science, computer science, engineering, or a related field.

The supervisor named in the linked faculty page is Debjani Sihi, Assistant Professor at North Carolina State University. Her research program focuses on the plant-soil-microbe-atmosphere continuum, integrating field and laboratory data with machine learning and process-based models, especially for soil carbon and nutrient cycles and greenhouse gas emissions.

To apply, prospective students should email an academic CV and a brief research interest statement to [email protected]. The position is expected to start in Fall 2026 or Spring 2027.

Funding details

Fully funded PhD position. The post states the student will be fully funded; no stipend amount, tuition details, or additional benefits are specified.

What's required

Applicants should have a BS or MS in earth system science, environmental science, soil science, data science, computer science, engineering, or a related field. Desired preparation includes programming experience in Python, R, Julia, Fortran, C++, or similar, and experience with ML/AI frameworks, statistical inference, ecosystem or biogeochemical modeling. Interest in process-based models, agroecosystem modeling, data fusion, and greenhouse gas flux prediction is preferred.

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Professors

Assistant Professor

University Name
Country flag

United States

Research Interests

Statistics

Soil Science

Biology

Computer Science

Earth Science