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scientific data handling Familiarity with: Multi-agent systems or autonomous systems Optimization under constraints Experience in: Knowledge graphs, NLP, or information extraction (a strong plus) Application
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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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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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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