Postdoctoral research fellowship: “Developing Advanced Weather Prediction Models for Agricultural Production Monitoring in Morocco”

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.

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.

Job Description

We are seeking a highly motivated and skilled Postdoctoral Research Fellow to join our multidisciplinary research team on the project "Crop Growth Monitoring and Yield Forecasting". This project aims to revolutionize agriculture in Morocco by combining cutting-edge technologies, including crop growth models, remote sensing data, data assimilation, machine learning, and seasonal weather forecasts. As a Postdoctoral Research Fellow, you will play a crucial role in developing and testing statistical models for the accurate forecasting of precipitation and temperature over Morocco.

Key Responsibilities

• Statistical model development: Led the development of advanced statistical models and machine learning algorithms for forecasting precipitation and temperature in Morocco.
• Work closely with the team to integrate various data sources into the modeling framework.
• Conduct rigorous testing and validation of the developed models.
• Collaborate with a diverse team of researchers, data scientists, agronomists, and remote sensing experts.
• Maintain comprehensive documentation of methodologies, code, and results.

Experience and Qualifications

• A Ph.D. in a relevant field, such as machine learning, climate science, environmental sciences, geoinformatics, or a related discipline.
• Proficiency in advanced learning techniques and statistical modeling.
• Strong programming skills in languages like Python or R.
• Professional experience in the application of Machine Learning algorithms in the mapping and correction of spatial data.
• Professional experience in data analysis, preprocessing, manipulation, and cleaning.
• Excellent communication and collaboration skills for interdisciplinary research.
• A track record of independent research and publications.
• Familiarity with climate data, remote sensing, and crop modeling is a plus.