Research Engineer Lead

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

Minimum Qualifications:

Requires a doctor of philosophy or veterinary medicine and three years of relevant experience. The degree requirement ensures candidates possess the research design expertise, data analysis proficiency and methodological rigor necessary to plan and execute studies, coordinate data collection and prepare documentation for publication. These competencies are typically developed and rigorously validated only through the comprehensive curriculum and intensive experiential experience inherent in a formal degree program.

Preferred Qualifications:

• Doctoral degree in agricultural engineering, biological systems engineering, agronomy, geography, remote sensing, data science, statistics, or a closely related field.


• At least 3 years of relevant experience in the following areas: precision agriculture, digital agronomy, plant breeding, high-throughput phenotyping, crop consulting, geospatial analysis, remote sensing, agricultural statistics, field research, agricultural data science, or analytical software development.


• Experience conducting applied research in digital agriculture, precision agriculture, plant breeding, phenotyping, crop production, or soil and water management.


• Experience working with UAV, satellite, GIS, GPS/GNSS, sensor, machinery, environmental, and agricultural field-trial datasets.


• Proficiency in Python, R, SQL, GIS, cloud computing, version control, and reproducible data-analysis workflows.


• Experience developing or testing geospatial, statistical, agricultural, or analytical software.


• Experience with raster processing, vector analysis, image analysis, remote sensing, spatial statistics, and database integration.


• Experience with regression, classification, clustering, machine learning, deep learning, time-series analysis, computer vision, image segmentation, or Bayesian methods.


• Experience using machine-learning and deep-learning frameworks.


• Experience with dashboard and visualization tools such as Streamlit, R Shiny, Tableau, Power BI, or comparable platforms.


• Knowledge of experimental design, agricultural statistics, crop growth stages, agronomic practices, yield-limiting factors, pest and disease management, nutrient management, irrigation management, soil and water management, and field variability.


• Experience supporting agricultural field research, UAV operations, GPS/GNSS systems, crop and soil sampling, sensors, and precision-agriculture equipment.


• Experience leading interdisciplinary projects and coordinating technical teams and external collaborators.


• Experience with grant writing, proposal development, technical reporting, scientific writing, workshops, demonstrations, or extension activities.


• Ability to translate user and stakeholder needs into technical specifications and practical analytical solutions.


• Strong written, verbal, analytical, organizational, project-management, and interpersonal skills.


• Ability to communicate technical findings to scientific, industry, producer, and public audiences.

Additional Role Information:

• Ability to work independently and collaboratively in interdisciplinary research and software-development environments.


• Ability to travel to agricultural research sites, stakeholder meetings, workshops, conferences, and partner locations, with anticipated travel of less than 10%.


• Ability to work outdoors under variable field and weather conditions and to operate or work around UAV systems, GPS/GNSS equipment, agricultural sensors, computers, and field-research equipment.


• A valid driver’s license, or the ability to obtain one, may be required for university business travel.