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, resource allocation, coordination, or distributed decision-making. Knowledge of wireless communication systems, communication-aware control, network optimization, or joint communication and sensing is highly
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control, network optimization, or joint communication and sensing is highly desirable. Experience with artificial intelligence, machine learning, reinforcement learning, or optimization techniques applied
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process intensification, sustainability, and advanced process control, aiming to develop AI-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex
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implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic pathway prediction. Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions
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computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials. The ideal candidate should have a strong background in artificial intelligence and
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to develop predictive models for polymer-based materials. This project aims to leverage computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials
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-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex chemical and biochemical processes. The research will contribute to next-generation smart
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
models for polymer-based materials. This project aims to leverage computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials. Key duties
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innovation for Morocco and Africa. With state-of-the-art infrastructure and a vast network of academic and industrial partners, UM6P is committed to advancing sustainable development and building a thriving
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-Guachi, L., Gomez-Mendoza, J.B., Revelo-Fuelagan, J. & Peluffo-Ordonez, D.H. (2021). Enhanced convolutional-neural-network architecture for crop classification. Applied Sciences, 11(9), 4292. Bhattacharya