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, atmospheric modeling, and deep learning. Research Focus Estimate cropland emissions (NH3 , N2 O, CO2 , CH4 ) using satellite observations, atmospheric chemistry models, and physics-informed deep learning
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monitoring agricultural emissions across Africa using satellite remote sensing, atmospheric modeling, and deep learning. Research Focus Estimate cropland emissions (NH3, N2O, CO2, CH4) using satellite
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conferences (e.g., NeurIPS, ICML, ACL, EMNLP, etc.). Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding
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projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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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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learning (RL) and deep reinforcement learning (DRL) for autonomous process management, dynamic resource distribution, and real-time decision-making. Design and deploy digital twins for integrated chemical
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Earth Observation data analysis and/or spatial modeling Proven ability to publish in high impact peer-reviewed international journals Experience with machine/deep learning / AI applied to environmental
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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
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Experience with machine/deep learning / AI applied to environmental or urban systems Familiarity with climate modeling, urban climate, urban agriculture, water resources, and energy systems Experience working