Overview
This internship presents a unique opportunity to engage in a project that integrates Earth observation data with advanced deep learning techniques to monitor food systems. Given the recent global disruptions affecting food flows, this initiative aims to enhance food security by analyzing and optimizing food distribution networks, particularly in Rwanda.
Background & Relevance
Food systems are increasingly vulnerable to global shocks, such as geopolitical conflicts and pandemics, which can disrupt supply chains and impact food prices. Understanding these dynamics is crucial, especially in regions where food security is already precarious. While significant progress has been made in utilizing Earth observation data to assess agricultural productivity, there remains a gap in analyzing the intermediate stages of food distribution. This internship aims to fill that gap by employing machine learning to model food flows and improve the resilience of food systems.
Key Details
- Duration: 6 months, starting February 2025
- Location: CIRAD, UMR TETIS, Montpellier, France
- Remuneration: Approximately 600 euros/month
- Datasets:
- Public Market Dataset: 1.2 million items across 70 markets
- CGIAR/IITA Survey Database: Monthly data from 7,000 vendors across 67 markets in Rwanda
- Main Tasks:
- Database integration and market mapping
- Geospatial data integration
- Machine learning model development
- Writing an internship report for potential scientific publication
Eligibility & Participation
This internship is suitable for students with a background in machine learning, data analysis, and programming. Candidates should possess a strong interest in food security and the application of AI in real-world scenarios. The position targets individuals eager to contribute to impactful research and policy development in food systems.
Submission or Application Guidelines
Interested candidates should submit their CV, a cover letter, and their M1 (or 4th year) transcript to the following emails:
– simon.madec@cirad.fr
– roberto.interdonato@cirad.fr
Please specify “CANDIDATURE STAGE DIGITAG” in the email subject line.
Additional Context / Real-World Relevance
The integration of AI and Earth observation data is transforming how we monitor and analyze food systems. By leveraging satellite imagery and machine learning, this internship aims to provide insights that can inform policy decisions and improve food security in vulnerable regions. The project highlights the importance of interdisciplinary approaches in addressing complex global challenges.
Conclusion
This internship offers a valuable chance for students to apply their skills in a meaningful context, contributing to the understanding and improvement of food systems in Rwanda. Interested individuals are encouraged to apply and be part of this innovative research initiative.
Category: Internships & Student Roles
Tags: earth observation, deep learning, food security, machine learning, geospatial data, remote sensing, data analysis, CIRAD, Rwanda
