Postdoctoral Researcher – Multimodal Crop Analysis & Fertilizer Optimization

About the role

We are seeking a post‑doctoral researcher to develop farmer‑centric systems that combine multi‑modal remote‑sensing, soil and phenology data for crop classification and precise fertilizer recommendations. The work will be based at UM6P’s Benguerir campus and will involve close collaboration with agronomists and industry partners.

Key responsibilities

• Design and implement pipelines that fuse optical, SAR, soil and phenology data for crop type mapping.
• Develop machine‑learning and deep‑learning models for spatio‑temporal analysis of satellite imagery (Sentinel, Landsat) and ground data.
• Generate customized fertilizer recommendation algorithms and validate them with field trials.
• Produce reproducible research outputs, including code, datasets and documentation.
• Publish findings in peer‑reviewed journals and present results to stakeholders.

Required profile

• PhD (awarded or defended before start) in Computer Science, Remote‑Sensing, Geoinformatics, Agricultural Data Science or a related discipline.
• Strong track record in remote‑sensing imagery, time‑series analysis and ML/DL for spatio‑temporal data.
• Ability to work independently and produce reproducible research.
• Good written and oral English; willingness to collaborate with agronomists.
• Preferred: post‑doc or ≥2 years research experience, first‑author publications, field/ground‑truth experience, and knowledge of fertilizer‑recommendation systems.

Required skills

• Python programming.
• Machine‑learning frameworks such as PyTorch or TensorFlow.
• Geospatial libraries (rasterio, GDAL).
• Google Earth Engine, Sentinel and Landsat data processing.
• Remote‑sensing, multimodal data fusion, time‑series and spatio‑temporal analysis.

What we offer

• Fixed‑term 24‑month contract.
• Access to high‑performance computing resources and satellite data platforms.
• Opportunities for interdisciplinary collaboration and publication.