CESFRA - Postdoctoral Researcher in Cropping Systems using Remote Sensing and Crop Models

Description About UM6P

Being located in the heart of the future Green City of Ben Guerir, Mohammed VI Polytechnic University (UM6P), is a higher learning institution with an international reputation; and committed to be a center of excellence in applied and problem-solving education, research and technology transfer for a sustainable economic development of Morocco in particular and the African continent in general.

Description About CESFRA-CAES

The Center of Excellence for Soil and Fertilizer Research in Africa (CESFRA) at the College of Agriculture and Environmental Science (CAES) of UM6P is established to serve and support the African nations in their undertakings of transforming the agricultural sector through fostering scientific and technological innovations. CESFRA, as an African-oriented center, has much to contribute and offer to the development of knowledge economy of the African continent so that the fabrics and settings of the traditional agriculture is modernized. CESFRA is seeking for a dedicated research assistant who will contribute to the on-going research activities in salt affected soils management.

Position Summary

The Center of Excellence for Soil and Fertilizer Research in Africa (CESFRA) at the College of Agriculture and Environmental Science (CAES) of UM6P invites applications for a postdoctoral position in cropping systems research using remote sensing and crop models. This position will focus on integrating remote sensing data with advanced crop models to enhance agricultural productivity and sustainability in key agricultural regions of Morocco. The successful candidate will work on a multidisciplinary project aimed at improving cropping systems, optimizing irrigation management, and assessing the impact of climate change on crop productivity.

Key Duties

Successful candidates are expected to:

• Acquire and analyze remote sensing data for key agricultural regions in Morocco and conduct field surveys.
• Develop and calibrate crop models to simulate and predict crop growth and yield.
• Apply machine learning algorithms to enhance crop model predictions using remote sensing data.
• Model and assess the impact of climate change on crop productivity.
• Develop strategies to optimize irrigation scheduling, fertilizer application, and sowing date management based on crop requirements.
• Prepare and publish research findings in high-impact journals and present at conferences.
• Collaborate with other researchers and supervise graduate students.

Criteria of the Candidates

• Criteria of the Candidates
  •    PhD in Agronomy, Soil Science, Remote Sensing, Agricultural Engineering, Environmental Science, or a related field.
  •    Proven experience in remote sensing data acquisition and analysis.
  •    Proficiency in crop modeling using tools.
  •    Strong background in machine learning and its application in agricultural research.
  •    Experience with climate change impact assessment on agricultural systems.
  •    Demonstrated ability to conduct independent research and produce high-quality scientific publications.
  •    Excellent data management and statistical analysis skills.
  •    Proficiency in programming languages (e.g., Python, R) and software used in remote sensing and crop modeling.
  •    Strong written and verbal communication skills.
  •    Ability to present research findings to diverse audiences.
  •    Experience working in collaborative and interdisciplinary research environments.
  •    Ability to work independently and manage multiple tasks effectively.
  •    Strong organizational and time management skills.