Pro-RUWA Call for Applications for upcoming courses
Course 1: GIS for beginners: GIS Beginner course aims to enhance basic knowledge and skills in GIS and remote sensing, such as creating a simple map, georeferencing, extracting information from Google Earth Pro, and analyzing and collecting spatial data for Agricultural sciences
Course 2: R-statistics: This course is designed for graduate students in agricultural sciences (MSc and PhD level) who want to learn the fundamentals of R programming and statistical analysis.
Course 3: An Introduction to Statistical Design and Modelling: The first half of the course is all about statistical design. We’ll explore which variables to measure, how to avoid biases and misinterpretations in your research, and why diversity is important in your study designs. We’ll additionally introduce the concept of a treatment variable, an element which defines an experiment.
In the second half of the course, we’ll focus more on modelling. You’ll learn how sample size impacts hypothesis testing and what those p-values actually mean. We’ll also cover modelling techniques to handle data variability and how to compare different models.
📝 Application details:
- Deadline for applications: 13 April 2026 (13:00 CET)
- Notification of results: 15 April 2026 and start the online course immediately
🔗 Application link:
Course 1: GIS for beginners (or copy: https://forms.gle/48K3PJrRprSpvUECA)
Course 2: R-statistics(or copy: https://forms.gle/nRNhhoKJZS5E4jKH7)
Course 3: An Introduction to Statistical Design and Modelling(or copy: https://forms.gle/nY2BZkAWPHgcyx9R6)
We look forward to receiving your applications. If you did not hear from us, that means we are fully booked and kindly ask for your understanding. If there is high demand, we will make our e-learning course available in Fall 2026.
Your Pro-RUWA2.0 team
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I am a PhD candidate focusing on pastoral system resilience in Uganda. My research uses a spatially explicit transition-matrix migration model to simulate livestock mobility, forage availability, and grazing pressure across seasons under changing conditions such as climate variability and land-use change. It integrates participatory data, GIS-based spatial units, and remotely sensed biomass to identify hotspots of forage deficit.
These courses in GIS, R statistics, and statistical modelling are directly relevant to my work and will strengthen my ability to analyze spatial data, implement models, and improve research design. I am committed to applying these skills in my PhD and future research.