24 distributed-algorithm-"Meta"-"Meta" Postdoctoral positions at MOHAMMED VI POLYTECHNIC UNIVERSITY
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algorithms in various projects. Contribute to the supervision of doctoral students and interns at the center. Participate in the training courses organized by the center. Work on collaborative projects with
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analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission management platforms, contributing to the deployment of smart UAV demonstrators under
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associated sediments. A particular emphasis will be placed on the distribution, speciation, and enrichment mechanisms of strategically important trace elements (e.g., rare earth elements, uranium) within
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computing, and digital twin technologies for mission autonomy and predictive analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission
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data for model calibration and validation. Apply sensitivity analysis and optimization algorithms to refine model parameters and improve predictive accuracy. Contribute to code development, documentation
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, the next step in this project is to address sparse optimization for tensors. We propose the integration of randomized algorithms into sparse optimization frameworks for the purpose of completing
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using simulation-based parametric studies. Couple computational results with experimental data for model calibration and validation. Apply sensitivity analysis and optimization algorithms to refine model
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Solutions at the UM6P Data Center. Innovate and improve image analysis algorithms for plant trait quantification. Collaborate with international research teams and contribute to joint publications and
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characterization, distribution, diversity, and transmission mechanisms between rhizobia and hostel plants. Main responsibilities: The selected candidate will be expected to: Conduct research as part of the project
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interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific computing and machine learning. The research will emphasize both theoretical