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/ Deep Learning Knowledge of: Active learning, Bayesian optimization Reinforcement learning or decision-making systems Experience with: Python ecosystem (PyTorch, Scikit-learn) Data pipelines and
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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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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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economic, energy efficiency, and environmental performance metrics. Utilize reinforcement learning (RL) and deep reinforcement learning (DRL) for autonomous process management, dynamic resource distribution
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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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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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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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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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pathway prediction. Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions, and identify novel synthetic routes. Curate and manage reaction datasets from literature
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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