Postdoctoral Researcher in Remote Sensing and ML for Crop Classification and Fertilizer Recommendations

About the recruiter — UM6P

Mohammed VI Polytechnic University (UM6P) is a research-and-innovation focused university in Morocco committed to African development. UM6P’s College of Computing (Benguerir & Rabat campuses) advances world-class research and education in Computer Science, fostering partnerships with industry and local stakeholders.

Project summary (one line)

Develop farmer-centric systems that fuse multi-modal remote sensing, soil and phenology data to enable crop classification and precise, customized fertilizer recommendations.

Selection criteria (short)

Required

• PhD (awarded or defended before start) in Computer Science, Remote-Sensing/Geoinformatics, Agricultural Data Science, or related field.
• Strong track record in remote-sensing imagery and/or time-series analysis and ML/DL for spatio-temporal data.
• Advanced Python skills and experience with ML frameworks and geospatial tools (e.g., PyTorch/TensorFlow, rasterio/GDAL).
• Ability to work independently and produce reproducible research outputs.
• Good English (written & oral) and willingness to collaborate with agronomists and partners.

Preferred

• Postdoc or ≥2 years research experience after PhD; first-author publications in relevant journals/conferences.
• Experience with multimodal data fusion (optical/SAR/soil/phenology), satellite platforms (Sentinel/Landsat/GEE), and building reproducible pipelines.
• Field/ground-truth experience, agronomic knowledge, or fertilizer-recommendation systems. French/Arabic useful for local engagement.

Application materials (required)

1. Cover letter (fit with CropID + available start date).
2. CV with links (ORCID, GitHub).
3. Research statement (1–2 pages) with a 12–18 month plan.
4. Up to 3 representative papers and links to code/datasets (if available).
5. 2–3 referee contacts.

Selection & timeline (brief)

Shortlist based on research fit, technical skills, and interdisciplinarity. Top candidates invited for a technical interview covering past projects, a 6-month plan, reproducibility practices, and farmer-translation. Appointment: fixed-term (24 months), UM6P (Benguerir).

References

• Moreno-Revelo, M.Y., Guachi-Guachi, L., Gomez-Mendoza, J.B., Revelo-Fuelagan, J. & Peluffo-Ordonez, D.H. (2021). Enhanced convolutional-neural-network architecture for crop classification. Applied Sciences, 11(9), 4292.
• Bhattacharya, S. & Pandey, M. (2024). PCFRIMDS: Smart Next-Generation Approach for Precision Crop and Fertilizer Recommendations Using Integrated Multimodal Data Fusion for Sustainable Agriculture. IEEE Transactions on Consumer Electronics.