CRSA: Postdoctoral Research in crop growth monitoring and yield estimation by assimilation (10284)

About UM6P:

Located at the heart of the future Green City of Benguerir, Mohammed VI Polytechnic University (UM6P), a higher education institution with international standards, is established to contribute to the development of Morocco and the African continent. Its vision is honed around research and innovation at the service of education and development. This unique nascent university, with its state-of-the-art campus and infrastructure, has woven a sound academic and research network, and its recruitment process is seeking high quality academics and professionals in order to boost its quality-oriented research environment in the metropolitan area of Marrakech.

About CRSA

CRSA is a transversal structure across several UM6P Programs. Research within the center is organized around several major areas that aim to ensure the challenging Food and Water security goal in Africa, with a special focus on developing methods/tools that use multi-source remotely sensed data. The research aims to improve our understanding of the integrated function of continental surfaces and their interactions with climate and humans, with emphasis on sustainable management of natural resources (soil, land, water, agriculture) in the context of Climate Change. One of the center’s goals is to provide a set of services and operational products to users (local, national and international) that aid in the decision support of water and food systems.

Job Descrip on:

We are seeking a highly motivated Postdoctoral Research Fellow to join our interdisciplinary team working on an innovative project aimed at improving crop growth monitoring and yield forecasting using crop growth models, machine learning, and data assimilation methods. The successful candidate will contribute to the development and implementation of advanced models and algorithms to integrate diverse data sources (ground measurements, remote sensing data, weather forecast…) for accurate and reliable crop growth monitoring and yield forecasts. This position offers an excellent opportunity for career development, working alongside leading experts in the field. 

Key Responsibilities:

• Develop and implement crop growth models to predict yield under varying environmental conditions. Apply machine learning and data assimilation techniques to integrate remote sensing, weather data, soil properties, and other relevant datasets.
• Validate and calibrate crop growth model using observed data, ensuring accuracy and reliability of predictions.
• Develop and implement hybrid models that combine prosses-based model and machine learning algorithms for crop yield forecasting.
• Collaborate with modelers, data scientists, and other stakeholders to translate model outputs into actionable insights for farmers and agricultural policymakers.
• Publish research findings in peer-reviewed journals and present at national and international conferences.
• Mentor and supervise graduate students, as required.

Experience and Qualifications:

• Ph.D. in Agronomy, Agricultural Engineering, Computer Science, or a related field.
• Strong background in crop modeling, machine learning, and data assimilation.
• Proficiency in programming (e.g., Python, R, MATLAB) and experience with machine learning frameworks (e.g., TensorFlow, PyTorch).
• Demonstrated ability to analyze large and complex datasets.
• Excellent problem-solving skills and the ability to work independently as well as in a collaborative team environment.
• Strong communication skills, with a proven track record of publishing research findings. Experience with remote sensing data and geospatial analysis tools is an asset.

How to apply:

Interested candidates are invited to submit a cover letter, curriculum vitae, a statement of research interests, and contact information for three references. The cover letter should clearly articulate how the candidate’s skills and experience align with the requirements of the position and the goals of the project.

UM6P