185 software-defined-network-phd Postdoctoral research jobs at MOHAMMED VI POLYTECHNIC UNIVERSITY in Morocco
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teaching, as well as applied research and innovation. UM6P boasts state-of-the-art infrastructure and an extensive academic and research network, and is dedicated to economic and human development
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teaching, as well as applied research and innovation. UM6P boasts state-of-the-art infrastructure and an extensive academic and research network, and is dedicated to economic and human development
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infrastructures to enhance energy management, transportation networks, and urban sustainability. Key Responsibilities Design and implement digital twins to monitor and optimize urban systems, including
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using advanced geological modeling tools such as Datamine software, Leapfrog Geo and Surfer. Supervise MSc/PhD students working on related tasks. Publish high-impact papers and contribute to funding
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Department Qualification Type: Postdoctoral position Location: Ben Guérir, Morocco Funding for: National/International Researchers Funding amount: To be defined Hours: Full Time Closes: Until filled Deadline
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research and innovation to support education and sustainable development, UM6P boasts a state-of-the-art campus and infrastructure. It has built a robust academic and research network and is committed
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journals in the field. Participate to the supervision of PhD students and research internships. Criteria of the candidate: PhD in the field of Cryptography, Computer security or any related field. Strong
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a wide network of universities and research centers around the continent in order to link real field issues with up-to-date science. The institute consists of a multidisciplinary team of agronomists
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development. With its state-of-the-art campus and infrastructure, this unique nascent university has woven a sound academic and research network, and its recruitment process is seeking high-quality academics
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real-world applications in green chemistry and industrial synthesis. Key Responsibilities: Develop and implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic