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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
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of process control strategies, including model predictive control (MPC), nonlinear control, and optimal control theory. Proficiency in programming languages (Python, MATLAB) and experience with AI/ML
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optimal control theory. Proficiency in programming languages (Python, MATLAB) and experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience in data-driven modeling, deep learning, and
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Science and Society, but also by examining the truth of scientific theories (this is the role of the epistemologist) and the impact of putting them into practice (this is the role of the researcher in