The University of Missouri is offering an opportunity for a motivated Ph.D. candidate to develop and refine machine-learning models for pasture biomass estimation using proximal and remote sensing (UAVs, satellite imagery), soil data, and weather analytics.
This high-impact project aims to enhance sustainability across US pasturelands, in collaboration with international partners at Cranfield University (UK).
Key Responsibilities
- Acquire, preprocess, and manage large spatiotemporal datasets (UAV/satellite imagery).
- Develop and validate advanced ML models in Python or R.
- Explore data-fusion approaches (e.g., radar + optical data) to improve biomass estimates.
- Publish findings in peer-reviewed journals and present at conferences.
Required Qualifications
- Proven skills in statistics/machine learning (Python/R).
- Familiarity with remote sensing (e.g., ArcGIS, Google Earth Engine, SentinelHub).
- Experience handling large datasets and running robust quantitative analyses.
- Demonstrated publication or conference presentation record.
Preferred (Desirable) Qualifications
- Pasture or crop biomass research experience.
- Radar data interpretation and UAV operation skills.
Funding & Duration
Annual Stipend: $37,000.00
Project Period: 3 years, with the possibility of extension to 4 years
Start Date: July-August 2025
Location: University of Missouri, Columbia (USA), with opportunities to visit Cranfield University (UK).
How to Apply
1. Cover letter outlining experience, interests, and fit for the project.
2. CV, including relevant publications and references.
3. Contact details for two referees.
Submit to: Dr. Bernardo Cândido (bernardocandido@missouri.edu)
Subject: “PhD Application”
Close Date: Open until filled.
Interviews starts on April 15th
For more information, contact:
Dr. Bernardo Cândido (bernardocandido@missouri.edu)
Dr. Rob Kallenbach (kallenbachr@missouri.edu)
