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. Mathematically, a network is represented by a graph, which is a collection of nodes that are connected to each other by edges. The nodes represent the objects of the network and the edges represent relationships
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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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development, social behavior, strategic governance, education, migration, or resilience. Introduce and apply tools such as agent-based modeling (ABM), system dynamics, network analysis, game theory, and
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with practical applications in PDEs, ODEs, discrete optimization, and control theory. Provide expertise in operations research methods (e.g., linear, nonlinear, integer, stochastic programming) and
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algorithms, optimization routines, and simulation-based decision tools. Support project-based learning with practical applications in PDEs, ODEs, discrete optimization, and control theory. Provide expertise in
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, analyze, and optimize new algorithms and system architectures for secure communication. Bridge theory and practice by developing simulation frameworks and/or contributing to experimental testbeds. Publish
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. Support project-based learning with practical applications in PDEs, ODEs, discrete optimization, and control theory. Provide expertise in operations research methods (e.g., linear, nonlinear, integer
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communication. Bridge theory and practice by developing simulation frameworks and/or contributing to experimental testbeds. Publish results in leading journals and present findings at top-tier international
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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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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient