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. This will involve data analysis, model design, and algorithm implementation.
• Work closely with the team to integrate various data sources into the modeling framework, including historical weather data, remote sensing data, and crop growth data. Ensure data quality, consistency, and compatibility with the models.
• Conduct rigorous testing and validation of the developed models. Compare model forecasts with actual weather conditions to assess accuracy and reliability. Continuously refine and improve the models based on validation results.
• Collaborate with a diverse team of researchers, data scientists, agronomists, and remote sensing experts to ensure seamless integration of your models into the broader project framework.
• Maintain comprehensive documentation of methodologies, code, and results. Contribute to the preparation of research papers, reports, and presentations to share findings with the scientific community and project stakeholders.

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.

How to apply

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