Diego Marcos
1 week ago
PhD Position in Vision-Language Foundation Models for Agriculture at Inria Montpellier (Machine Learning, Earth Observation) Inria Montpellier in France
Degree Level
PhD
Field of study
Computer Science
Funding
Full funding availableCountry
France
University
Inria

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About this position
Join the EVERGREEN team at Inria Montpellier as a PhD student working on vision-language foundation models for agriculture. This position is part of the EU project AgriScienceFM, which brings together leading institutions such as Wageningen University & Research, the Computer Vision Center in Barcelona, and Mila - Quebec Artificial Intelligence Institute. The research will focus on developing and applying advanced machine learning techniques to heterogeneous and multi-temporal Earth observation data, addressing agro-environmental challenges like land cover mapping, deforestation monitoring, forest variable estimation, and yield prediction.
The EVERGREEN team specializes in designing machine learning models tailored for Earth observation data, exploring new learning paradigms, and enhancing the interpretability of AI systems. The group collaborates internationally with academic and industrial partners, providing a rich interdisciplinary environment. The PhD project will involve research on transferability of multi-modal classification models, low-data regime learning, and explainable AI for both image and time series data, with a strong emphasis on sustainable agriculture and environmental monitoring.
Applicants should have a solid background in machine learning, computer vision, or related fields, with experience in remote sensing or agricultural applications considered a plus. The position is fully funded as part of the AgriScienceFM project. For more information and application instructions, visit the EVERGREEN team website.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
How to apply
Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.
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