Postdoctoral Researcher in Multimodal Crop Analysis & Fertilizer Optimization

About the role

The Centre for Computational Agriculture (COLCOM) at UM6P seeks a postdoctoral researcher to develop farmer‑centric systems that combine multi‑modal remote sensing, soil, and phenology data. The work will enable accurate crop classification and generate precise, customized fertilizer recommendations for African agriculture.

Key responsibilities

• Design and implement reproducible pipelines for processing optical, SAR, soil, and phenology datasets.
• Fuse multi‑modal data using advanced machine‑learning and deep‑learning techniques.
• Develop spatio‑temporal models for crop classification and fertilizer recommendation.
• Collaborate with agronomists, local partners, and industry stakeholders to translate research outcomes into actionable tools for farmers.
• Publish findings in peer‑reviewed journals and present at international conferences.

Required profile

• PhD (awarded or defended before start) in Computer Science, Remote‑Sensing/Geoinformatics, Agricultural Data Science, or a related field.
• 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 outputs.
• Excellent written and oral English; willingness to collaborate with agronomists.

Required skills

• Advanced Python programming.
• Experience with ML frameworks such as PyTorch or TensorFlow.
• Proficiency with geospatial tools (rasterio, GDAL).
• Knowledge of satellite platforms (Sentinel, Landsat, Google Earth Engine).
• Expertise in multimodal data fusion and spatio‑temporal analysis.

What we offer

• Fixed‑term 24‑month contract at UM6P Benguerir campus.
• Access to high‑performance computing resources and extensive satellite data archives.
• Opportunity to work in a multidisciplinary team with leading agronomists and industry partners.