CRSA - Assistant Professor in Fertigation using Multispectral Remote Sensing, IoT and AI

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 Remote Sensing Center:

The Newly established Excellence Center of Remote Sensing Applications is a transversal structure across several UM6P Programs, i.e. The International Water Research Institute (IWRI), Soil science soil program (CESFRA) and agro-bioscience (AGBS) programs. The main goal of the center is to set up an unprecedented program of international level in terms of research and education and innovation. 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 projects aim to improve our understanding of the integrated functioning of continental surfaces and their interaction with climate and humans, with emphasis on sustainable management of natural resources (soil, land, water, agriculture) in the context of Climate Change. One of center’s aims 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 description:

The Center for Remote Sensing Applications (CRSA) at Mohammed VI Polytechnic University is seeking to hire an assistant professor with expertise in the field of agricultural and Fertigation engineering and process modeling. The ideal candidate should have a PhD in Agronomy, Agricultural engineering, Environmental Science or a related field, with a strong research record and teaching experience in the areas of agricultural science, irrigation systems engineering, and/or precision agriculture and experience with the use of simulation/modeling of crop development and water flow through agricultural systems. The successful candidate is expected to further build up research capacity in the above-mentioned field at CRSA and to contribute in developing a cutting-edge research program in optimization of fertigation using multispectral remote sensing, IoT, AI and process modeling. He will also contribute to undergraduate and graduate teaching in the department. Responsibilities will include conducting research, teaching, developing innovative courses, supervising graduate students, and providing service to the university and the profession. The Center entity operates on the principle of partnerships with international and Moroccan Institutions and Universities. The holder of the position is expected to collaborate with the CRSA team and partners toward the launch of an ‘’Executive Master in Remote sensing application for soil, water and agriculture, and will contribute to a research Master on Water Science and Agriculture Sciences.

Key duties:

Successful candidates are expected to have the capacity to:

• Developing cutting-edge research projects that focus on the use of remote sensing and artificial intelligence (AI) to optimize fertilizer and irrigation systems for agricultural production.
• Determining and implementing appropriate remote sensing and AI techniques to interpret crop growth and development and assess the effects of fertilizer and irrigation on crop productivity.
• Investigating the application of remote sensing and AI techniques in precision agriculture.
• Preparing written reports and presentations to communicate research results. Teaching courses related to remote sensing, GIS and precision agriculture.
• Using different communication and disseminations tools (peer-reviewed publications, large public dissemination …). 
• Pursue permanent trainings in order to improve up-to-date capacities.
• Supervising and advise Master and PhDs students to support established Ph.D. programs
• Collaborating with other faculty on projects related to remote sensing and precision agriculture.

Selection Criteria

The successful candidate must have:

General Criteria

• PhD in Remote Sensing, Agronomy, Environmental Sciences, or a related area.
• A minimum of 2-yers Postdoctoral research experience within the academic sector. An equivalent experience with industry, government, and/or academic sectors is an asset.
• Experience in leading and conducting scientific research projects in Remote Sensing and AI applications to multidisciplinary disciplines.
• Demonstrated experience in national/international multidisciplinary collaborating researches.
• Demonstrate understanding of operational requirements for a successful research project (such as satellite/UAV data acquisitions and processing).
• Ability to generate new ideas, links, and to build upon existing models to develop novel approaches in natural resources monitoring (for water, soil and plant).
• Demonstrate knowledge in radiative and transfer surface models to invert biophysical parameters using multi-scale, multi-spectral and multi-sensor satellite/UAV-based data.
• Experience in planning, operating and implementing field campaigns using ground and UAV measurements.

Specific Criteria for Assistant Professor

• Demonstrated excellence in research and commitment to teaching at the university level
• Quality of scientific achievements and impact of publications normalized by career stage.
• A good record of publications in peer-reviewed journals > 10.
• Contribution and presentations of research results in national and international conferences (> 10)
• Experience in supervising and/or co-supervision of university level students.
• Demonstrated teaching experience (i.e., three courses).

Skills:

Research skills

• Knowledge and experience with satellite imagery, lidar, and other remote sensing data sources
• Experience in programming and software for data analysis, such as Python, MATLAB and QGIS
• Capability to develop artificial intelligence algorithms, such as neural networks and decision trees
• Ability to develop models to simulate and analyze different scenarios of irrigation and fertilization.
• Knowledge of agronomic principles and crop production practices
• Ability to communicate complex concepts to stakeholders and policymakers.