CEDOC UM6P CESFRA : Enhancing Sustainable Agricultural Watershed Management in North Morocco through - Benguerir
Company sector : Education / Training
COMPANY :
Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned universities in its fields The University is engaged in economic and human development and puts research and innovation at the forefront of African development. A mechanism that enables it to consolidate Morocco’s frontline position in these fields, in a unique partnership-based approach and boosting skills training relevant for the future of Africa. Located in the municipality of Benguerir, in the very heart of the Green City, Mohammed VI Polytechnic University aspires to leave its mark nationally, continentally, and globally.
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Company culture :
UM6P – Mohammed VI Polytechnic University operates within a strongly innovation-driven culture, where creativity, experimentation and intellectual boldness are key foundations. The university promotes initiative, openness and autonomy in a stimulating environment focused on research, entrepreneurship and impact. This innovative mindset is reinforced by a solid cooperative dimension, fostering collaboration, knowledge sharing and human development. A clear focus on performance and excellence further supports this culture, while formal processes remain lighter to maintain agility and flexibility.
Job :
PhD Subject: Enhancing Sustainable Agricultural Watershed Management in North Morocco through Integrated Modeling and Machine Learning Approaches.
Job Qualifications:
- Engineer or master’s degree in a relevant field such as Environmental Science, Agricultural Engineering or a related discipline.
- Strong academic background with coursework in hydrological modeling, remote sensing, machine learning, and environmental management.
- Proficiency in programming languages such as Python, R, or MATLAB for data analysis and modeling purposes.
- Excellent written and verbal communication skills in English and French.
- Ability to work independently and collaboratively within a multidisciplinary research team.
Relevant experience consisting of:
- Prior experience with modeling software and remote sensing techniques for environmental monitoring.
- Familiarity with machine learning algorithms and their application to geospatial data analysis.
- Experience in fieldwork, including sampling and data collection related to agricultural watershed management, soil erosion, or climate change adaptation.
- Participation in research projects or internships involving the collection and analysis of environmental datasets is desirable.
Key competences:
- Proficient in conducting literature reviews, experimental design, and data interpretation.
- Ability to develop and implement complex modeling frameworks for simulating hydrological processes, crop growth, and land use changes.
- Strong analytical skills for processing and analyzing large datasets from diverse sources, including field observations and remote sensing data.
- Capacity to communicate research findings effectively through written reports, presentations, and academic publications.
- Aptitude for problem-solving and critical thinking to address challenges in sustainable watershed management.
Personal Attributes:
- Demonstrates adaptability and resilience in fieldwork settings, maintaining focus and efficiency during data collection activities.
- Takes initiative to learn and problem-solve, seeking practical solutions to challenges encountered during research.
- Communicates effectively with team members and collaborators, fostering positive working relationships.
- Manages time effectively to meet project deadlines and deliver results within established timelines.
- Shows a genuine interest in environmental sustainability and a commitment to conducting research with integrity and professionalism.
Required profile :
Job Qualifications:
- Engineer or master’s degree in a relevant field such as Environmental Science, Agricultural Engineering or a related discipline.
- Strong academic background with coursework in hydrological modeling, remote sensing, machine learning, and environmental management.
- Proficiency in programming languages such as Python, R, or MATLAB for data analysis and modeling purposes.
- Excellent written and verbal communication skills in English and French.
- Ability to work independently and collaboratively within a multidisciplinary research team.
Relevant experience consisting of:
- Prior experience with modeling software and remote sensing techniques for environmental monitoring.
- Familiarity with machine learning algorithms and their application to geospatial data analysis.
- Experience in fieldwork, including sampling and data collection related to agricultural watershed management, soil erosion, or climate change adaptation.
- Participation in research projects or internships involving the collection and analysis of environmental datasets is desirable.
Key competences:
- Proficient in conducting literature reviews, experimental design, and data interpretation.
- Ability to develop and implement complex modeling frameworks for simulating hydrological processes, crop growth, and land use changes.
- Strong analytical skills for processing and analyzing large datasets from diverse sources, including field observations and remote sensing data.
- Capacity to communicate research findings effectively through written reports, presentations, and academic publications.
- Aptitude for problem-solving and critical thinking to address challenges in sustainable watershed management.
Personal Attributes:
- Demonstrates adaptability and resilience in fieldwork settings, maintaining focus and efficiency during data collection activities.
- Takes initiative to learn and problem-solve, seeking practical solutions to challenges encountered during research.
- Communicates effectively with team members and collaborators, fostering positive working relationships.
- Manages time effectively to meet project deadlines and deliver results within established timelines.
- Shows a genuine interest in environmental sustainability and a commitment to conducting research with integrity and professionalism.
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