The Center for Remote Sensing Applications (CRSA) at Mohammed VI Polytechnic University is looking to fill an Assistant Professor position in the area of multi-sensor remote sensing to support natural resources management, namely application to water, soil and agriculture. Candidates should combine independence, ambition and team work in the front line of research fields with a strong focus on innovation in both academic and applied research, in close collaboration with international academic and institutional partners.
The position is open to individuals with a PhD degree from a recognized university and with substantial expertise in the target fields. Previous high impact research and teaching expertise will be well appreciated. Good oral and writing communication skills in French and English represent a significant benefit. 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.
Successful candidates are expected to have the capacity to:
Teach graduate students in remote sensing, spatial analysis, data science, Land Surface
modelling, open-source GIS, GIS programming. Teaching classroom-based and online lectures and project work experiences are an asset.
- Combine land surface models and remote sensing data for agricultural resource management
3. Develop ambitious interdisciplinary research projects using multi-sensor remote sensing
4. Use different communication and disseminations tools (peer-reviewed publications, large public dissemination …).
- Pursue permanent trainings in order to improve up-to-date capacities.
- Supervise and advise Master and PhDs students to support established Ph.D. programs
- Work and collaborate closely with researchers (i.e., Faculty, Research, and postdoctoral fellows)
Applications must contain:
- A cover letter indicating the position applied and the main research interests,
- A detailed CV,
- A brief research statement,
- Contact information of 3 references (applicants are assumed to have obtained their references consent to be contacted for this matter).
The shortlisted candidates will be invited to meet the university selection committee.
Desired skills and experience:
Criteria of the candidate:
- PhD in Remote Sensing, Geospatial Science, Environmental Sciences, or a related area.
2. 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.
3. Experience in leading and conducting scientific research projects in Remote Sensing applications to multidisciplinary disciplines.
- Demonstrated experience in national/international multidisciplinary collaborating researches.
5. 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).
7. Demonstrate knowledge in radiative and transfer surface models to invert biophysical parameters using multi-scale, multi-spectral and multi-sensor satellite/UAV-based data.
8. Experience in planning, operating and implementing field campaigns using ground and UAV measurements.
- Demonstrated excellence in research and commitment to teaching at the university level
2. Quality of scientific achievements and impact of publications normalized by career stage.
3. 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).
Research skills :
- Surface biophysical properties inversion.
- Multi-spectral/multi-resolution remote sensing analysis.
- Time series analysis and machine learning approaches.
4. Land surface water and hydrological and/or hydro-ecological modelling.
5. Modern programming languages (R, IDL, MATLAB, Python, C++, etc.).