Lead Research Engineer, Digital Agriculture & Geospatial AI

Kansas State University; Kansas; On-site; USD 65,000 - 105,000; Full time

Job summary

Kansas State University invites applications for a doctoral-level research position in agriculture data science and geospatial analytics. The role focuses on designing and executing interdisciplinary studies, coordinating data collection, and integrating advanced analytics to support crop production and resource management.

The candidate will work with interdisciplinary teams, develop reproducible workflows, and contribute to publications and grant activities, leveraging Python, R, GIS, and

Qualifications

• PhD or DVM required with 3+ years of relevant experience.
• Experience in precision agriculture, digital agronomy, plant breeding, phenotyping, crop production, or soil and water management.
• Proficiency in Python, R, SQL, GIS, cloud computing, version control, and reproducible data-analysis workflows.
• Ability to translate user and stakeholder needs into technical specifications and practical analytical solutions.

Responsibilities

• Lead interdisciplinary research projects and coordinate technical teams and external collaborators.
• Coordinate data collection, data quality control and documentation for publication.
• Develop and test geospatial, statistical, agricultural, or analytical software.
• Prepare grant proposals, technical reports, and research communications.

Skills

Python

R

SQL

GIS

Remote sensing

Machine learning

Data analysis

Statistical modeling

Data visualization

Cloud computing

Version control

Scientific writing

Project management

Interdisciplinary collaboration

Education

Doctor of Philosophy (PhD)

Doctor of Veterinary Medicine (DVM)

Tools

Streamlit

R Shiny

Tableau

Power BI

GIS software

Cloud computing

Version control

Job description

Kansas State University invites applications for a doctoral-level research position in agriculture data science and geospatial analytics. The role focuses on designing and executing interdisciplinary studies, coordinating data collection, and integrating advanced analytics to support crop production and resource management.

The candidate will work with interdisciplinary teams, develop reproducible workflows, and contribute to publications and grant activities, leveraging Python, R, GIS, and