187 digital-communication Postdoctoral positions at MOHAMMED VI POLYTECHNIC UNIVERSITY
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. Nestled in Benguérir’s Green City, our multidisciplinary campus forges strong partnerships with industry, government, and communities to deliver real-world solutions for a sustainable future. The Green
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As part of the project "Digital Twin for Planning Under Uncertainty" , we are seeking a postdoctoral researcher to develop a digital twin aimed at enhancing the planning of OCP’s production activities
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Institute (GTI) at UM6P specializes in: Renewable energy systems (solar, wind, smart grids) Advanced digital technologies (Industry 4.0, IoT, automation) Green process engineering (circular-economy and low
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. Promote research and development findings through publications in high-impact Q1 scientific journals and indexed conferences. Digitalization and Integration of Optimization Tools: Integrate the developed
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. Responsibilities of the Position The Postdoctoral researcher is intended to support the soil spectroscopy research activities and digital soil mapping with specific responsibilities as presented below: Conduct
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such as green infrastructure. Utilize digital tools (GIS, hydrological models) to analyze and visualize urban flood risks, droughts, and water flows. Assess the effects of water management strategies
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microscope, XRF, XRD, SEM-EDS, EPMA, ICP-MS). Publication record in peer-reviewed scientific journals. Excellent oral and written communication skills in French/English Required documents include
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the broader research community. Experience and Qualifications PhD in Soil Science, Remote Sensing, Environmental Science or a related field. Strong experience in Soil sciences (e.g., physics, chemistry
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learning, imitation learning, and the integration of large language, vision–language, and vision–language–action models to improve generalization. A key objective is to design lightweight and efficient
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language, vision–language, and vision–language–action models to improve generalization. A key objective is to design lightweight and efficient learning architectures suitable for deployment on resource