We are looking for a highly motivated candidate with:
1. Master’s degree in environmental science, remote sensing, computer science, AI/deep learning, or a related discipline.
2. A strong background in quantitative data analysis and programming, with (the ability to develop) expertise in machine learning, deep learning, and image processing.
3. Proficiency in coding (e.g. Python). Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) is an asset.
4. Excellent analytical and problem-solving skills.
5. Strong written and verbal communication skills in English.
6. Interest in interdisciplinary research, including fieldwork and collaboration across AI, remote sensing, and ecology.
Candidates who do not yet have experience in deep learning or image processing but have a strong analytical background and the ability to acquire these skills are encouraged to apply. Monitoring reforestation success in semi-arid regions is crucial for combating land degradation and climate change. Traditional monitoring methods are labor-intensive and lack scalability. This project aims to overcome these challenges by leveraging high-resolution drone and satellite imagery, combined with advanced machine learning algorithms, to develop spatially explicit models of reforestation success. The goal is to create AI models that accurately estimate tree counts, biomass dynamics, and biodiversity, supporting effective and scalable reforestation efforts. Responsibilities:
7. Data Collection & Integration: Acquire, preprocess, and integrate high-resolution datasets from drone and multispectral satellite imagery, ensuring data quality and consistency. Conduct fieldwork in Senegal in collaboration with Lignaverda.
8. AI Model Development: Design, refine and train deep learning models to extract tree counts, biodiversity indicators, and biomass estimates from high-resolution imagery. Validate models with ground-truth data.
9. Scalability & Validation: Assess model robustness across different spatial scales, conducting sensitivity analyses to simulate satellite imagery conditions and test upscaling potential.
10. Research & Dissemination: Publish findings in high-impact scientific journals and present results at conferences.
11. Collaboration & Supervision: Work closely with interdisciplinary research teams, co-supervise MSc students, and contribute to the research environment.
Are you passionate about using AI to make a real-world environmental impact? Do you want to work at the intersection of deep learning, remote sensing, and sustainability? We invite applications for a fully funded PhD position at KU Leuven, where you'll develop AI-driven tools to monitor reforestation success in semi-arid regions. This interdisciplinary project offers hands-on fieldwork in Senegal, access to cutting-edge AI and remote sensing technologies, and collaboration with leading experts across multiple disciplines. Join us in advancing sustainable reforestation through AI! The PhD position is part of the "Chair for AI-based remote sensing for monitoring reforestation success in semi-arid regions", a multi-faculty initiative led by Prof. Stef Lhermitte (Earth and Environmental Sciences), in collaboration Prof. Ben Somers (Earth and Environmental Sciences), Prof. Matthew Blaschko (Electrical Engineering), and Prof. Toon Goedemé (Electrical Engineering). The project is conducted under the Leuven.AI initiative and in close collaboration with Lignaverda, with fieldwork opportunities in Senegal. Lignaverda (lignaverda.org) is a non-profit organization (International vzw) that promotes sustainable, biodiverse tree planting and long-term forest stewardship.What We Offer:
12. A fully funded 4-year PhD position within a dynamic and internationally recognized research environment under supervision of 4 (co-)promoters with diverse research expertise.
13. Opportunities to work with top research groups at KU Leuven, including Earth and Environmental Sciences and Electrical Engineering (ESAT).
14. Hands-on experience with AI-driven remote sensing technologies and real-world applications.
15. Fieldwork opportunities in Senegal in collaboration with Lignaverda.
16. Access to state-of-the-art research facilities and resources.
17. Support for publishing in leading scientific journals and attending international conferences.
18. A vibrant academic community in Leuven, a historical university town at the heart of Western Europe.