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Assist. Prof in Next-Generation Approaches of Remote Sensing for Applications to Digital Agriculture
Assist. Prof in Next-Generation Approaches of Remote Sensing for Applications to Digital Agriculture Mohammed VI Polytechnic University (UM6P) is an internationally oriented institution of higher
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Assist Prof in Next-Generation Approaches of Remote Sensing for Applications to digital soil mapping
the challenging food security/sustainable agriculture, water and environmental goal in Africa, with a special focus on developing actionable and impactful methods/tools that use multi-source remotely sensed data
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in Africa, with a special focus on developing actionable and impactful methods/tools that use multi-source remotely sensed data. The research aims to improve our understanding of the integrated
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in Africa, with a special focus on developing actionable and impactful methods/tools that use multi-source remotely sensed data. The research aims to improve our understanding of the integrated
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in the context of global sustainability standards. Key Responsibilities: Research and Development: Conduct cutting-edge research that utilizes GIS, remote sensing, and other spatial technologies
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developing methods/tools that use multisource remotely sensed data. The research aims to improve our understanding of the integrated functioning of continental surfaces and their interaction with climate and
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of GIS, remote sensing, and other analytical software tools. Excellent analytical, organizational, and problem-solving skills. Strong publication record in relevant academic journals. Excellent
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and remote sensing, Agricultural Intelligence related to degraded soil is highly desirable. Potential research areas will address current issues related to mine and marginal soil rehabilitation, and
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modeling or other quantitative techniques (data synthesis and assimilation, remote sensing) are also welcomed. Selected candidate will be requested to develop an innovative research activity, particularly in
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management practice (fertilization, tillage, and erosion, and water use). Complementary modeling or other quantitative techniques (data synthesis and assimilation, remote sensing) are also welcomed. Selected