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Lin Meng

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Fully Funded PhD in Vegetation Remote Sensing, Urban Ecology, Environmental Change, and AI/ML at Vanderbilt University Vanderbilt University in United States

Degree Level

PhD

Field of study

Computer Science

Funding

The PhD position is described as fully funded. No stipend amount or further details about tuition, fees, health insurance, or other benefits are provided in the post.

Deadline

Aug 1, 2027

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Country

United States

University

Vanderbilt University

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Keywords

Computer Science
Environmental Science
Geography
Artificial Intelligence
Urban Ecology
Phenology
Tropical Biology
Light Pollution
ML

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About this position

Dr. Lin Meng, Assistant Professor at Vanderbilt University, is recruiting a fully funded PhD student to join her lab in Fall 2027.

The research focuses on vegetation remote sensing, urban ecology, environmental change, and artificial intelligence/machine learning. Related interests highlighted in the profile include phenology, climate, tropical ecology, and light pollution. The project is suited to applicants interested in environmental science, geography, remote sensing, ecological change, and computational methods.

The post states that the position is fully funded, but does not specify the stipend, tuition coverage, benefits, eligibility requirements, or application deadline beyond the Fall 2027 intake. Prospective applicants should consult the lab’s joining page for detailed requirements and application instructions.

Funding details

The PhD position is described as fully funded. No stipend amount or further details about tuition, fees, health insurance, or other benefits are provided in the post.

How to apply

Review the position details and application instructions at Lin Meng’s lab page: https://sites.google.com/view/linmeng/join. Apply for the Fall 2027 PhD opportunity according to the instructions provided there.

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Professors

Assistant Professor

University Name
Country flag

United States

Research Interests

Climatology

Ecology

Phenology

Plant Ecology

Climate Dynamics