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Mohammed VI Polytechnic University

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Postdoctoral Researcher in Multimodal Crop Analysis and Fertilizer Optimization Mohammed VI Polytechnic University in Morocco

Degree Level

Postdoc

Field of study

Computer Science

Funding

Available

Deadline

Dec 4, 2026

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Country

Morocco

University

Mohammed VI Polytechnic University

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Keywords

Computer Science
Environmental Science
Agriculture
Deep Learning
Geography
Spatial Analysis
Precision Farming

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About this position

Mohammed VI Polytechnic University (UM6P) is seeking a postdoctoral researcher for COLCOM, a research project focused on multimodal crop analysis and fertilizer optimization. Based at UM6P in Benguerir, Morocco, the work is situated within the university’s research-and-innovation environment and its College of Computing, which advances computer science research and collaborates with industry and local stakeholders.

The project aims to develop farmer-centric systems that combine multimodal remote sensing with soil and phenology data. The researcher will contribute to crop classification and precise, customized fertilizer recommendations, supporting more sustainable and data-driven agricultural decision-making.

The research involves spatio-temporal data analysis, machine learning and deep learning, satellite imagery, and geospatial computing. Potential data sources and methods include optical and SAR imagery, soil and phenology information, Sentinel and Landsat platforms, Google Earth Engine, and reproducible computational pipelines. The role also connects computational research with agronomy, field or ground-truth information, and the practical translation of models into systems useful to farmers.

The successful researcher will work independently while collaborating with agronomists and project partners. The position offers an interdisciplinary setting connecting computer science, remote sensing, geoinformatics, agricultural data science, environmental monitoring, and precision agriculture.

Funding details

Available

What's required

Applicants must hold a PhD, awarded or defended before the start date, in Computer Science, remote sensing or geoinformatics, agricultural data science, or a related field. Required experience includes remote-sensing imagery and/or time-series analysis, machine learning or deep learning for spatio-temporal data, advanced Python, and relevant ML and geospatial tools such as PyTorch, TensorFlow, rasterio, or GDAL. Applicants should be able to work independently, produce reproducible research outputs, communicate well in written and spoken English, and collaborate with agronomists and partners. Preferred qualifications include postdoctoral or at least two years of post-PhD research experience, first-author publications, multimodal data fusion involving optical, SAR, soil, or phenology data, experience with Sentinel, Landsat, or Google Earth Engine, reproducible pipeline development, field or ground-truth experience, agronomic knowledge, and fertilizer-recommendation systems. French or Arabic is useful for local engagement. Application materials include a cover letter, CV with ORCID and GitHub links, a 1–2-page research statement with a 12–18-month plan, up to three representative papers and code or dataset links if available, and two or three referee contacts.

How to apply

Apply through the View or Apply link. Submit the required cover letter, CV, research statement, representative papers or code/dataset links if available, and two or three referee contacts. Include your available start date and explain your fit with CropID.

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