49 mathematics-graph-theory positions at MOHAMMED VI POLYTECHNIC UNIVERSITY in Morocco
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communication. Bridge theory and practice by developing simulation frameworks and/or contributing to experimental testbeds. Publish results in leading journals and present findings at top-tier international
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.). Contribute to institutional efforts in building AI tutors, learning analytics, and knowledge graphs to enhance student experience. Publish research and co-author student-led publications resulting from WiP
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, DeepChem, Gaussian, ORCA, ChemML). Knowledge of modern machine learning techniques (e.g., deep learning, graph neural networks, generative models). Interest in interdisciplinary research and/or real-world
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.). Contribute to institutional efforts in building AI tutors, learning analytics, and knowledge graphs to enhance student experience. Publish research and co-author student-led publications resulting from WiP
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interpreting datasets from various domains (health, agriculture, energy, logistics, etc.). Contribute to institutional efforts in building AI tutors, learning analytics, and knowledge graphs to enhance student
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performing quantum mechanical calculations (e.g., first principles density functional theory (DFT)) to elucidate complex reaction mechanisms occurring at the surface of various materials. Key Responsibilities
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to Semiconductor Physics: Semiconductor materials, band theory, charge transport, and devices. Microwave and RF Engineering: Principles of microwave propagation, waveguides, and RF components. Electronics Lab
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: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph
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23 Oct 2025 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Computer science Mathematics Researcher Profile Recognised Researcher (R2) Established Researcher
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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