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axes: AI-driven territorial diagnostics and foresight, integrating multi-source satellite data with machine learning and spatial modeling Climate–water–energy–agriculture interactions, with applications
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. • Contribute to interdisciplinary research projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep
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, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases
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issues. Proficiency in urban modeling tools such as MATLAB, Python (especially libraries like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. Advanced skills in predictive modeling and machine learning
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assimilation, machine learning, and seasonal weather forecasts. As a Postdoctoral Research Fellow, you will play a crucial role in developing and testing statistical models for the accurate forecasting
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to revolutionize agriculture in Morocco by combining cutting-edge technologies, including crop growth models, remote sensing data, data assimilation, machine learning, and seasonal weather forecasts. As a
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks and grey-box modeling) to enable predictive simulations of chemical and biochemical
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a two-year research project in collaboration with international academic partners. The main objective of the project is to study the interaction between machine learning and wireless communication
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of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in design and very good programming skills (Python, Pytorch