Research Scientist II, Step 1-4

Earth Day; Huntsville (AL); On-site; USD 90,000 - 120,000; Full time

Job summary

The EarthRISE Project Office at the University of Alabama in Huntsville seeks a Research Scientist with expertise in agricultural remote sensing, geospatial AI/ML, and applied decision-support development. The role focuses on turning multi-source Earth observations into practical tools for agriculture and land management.

Candidates should have strong Python/R skills, experience with cloud-based geospatial platforms, and spatial machine-learning workflows.

Qualifications

• PhD in a relevant discipline preferred.
• Strong agricultural remote sensing experience.
• Proficiency in Python and R; cloud-based geospatial platforms.

Responsibilities

• Apply multi-source Earth observations and geospatial analytics to agricultural monitoring and crop production systems.
• Model and develop AI/ML workflows for drought and crop-water applications.
• Develop scalable, actionable Earth observation products for operational and stakeholder use.
• Collaborate with stakeholders and project teams to implement decision-support tools.

Skills

Agricultural remote sensing

Python

R

Geospatial AI/ML

Applied decision-support development

Education

PhD in a relevant discipline

Tools

Cloud-based geospatial platforms

Spatial machine-learning workflows

Job description

The EarthRISE Project Office within the University of Alabama in Huntsville is seeking a Research Scientist with expertise in agricultural remote sensing, geospatial AI/ML, and applied decision-support development.

The position will apply multi-source Earth observations, geospatial analytics, modeling, and AI/ML to agricultural monitoring, crop production systems, land management, drought and crop-water applications, and agricultural decision-making. The role emphasizes developing scalable, actionable Earth observation products for operational and stakeholder use.

Candidates with strong agricultural remote sensing experience and proficiency in Python, R, cloud-based geospatial platforms, and/or spatial machine-learning workflows are encouraged to apply. A PhD in a relevant discipline is preferred.