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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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. 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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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 between objects. A common
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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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of Machine Learning (Theory or Practice). A successful candidate will be expected to lead a research team of graduate students as well as teach at the undergraduate and graduate levels. The position is open to
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Sciences (Theory or Practice). A successful candidate will be expected to lead a research team of graduate students as well as teach at the undergraduate and graduate levels. The position is open to
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theory and its transformative applications. Why Join the UM6P Vanguard Center? The UM6P Vanguard Center offers a unique environment that bridges the gap between theoretical research and impactful
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Cluster Contact: Pr. Johan Jacquemin – johan.jacquemin@um6p.ma Research Activities Develop independent research programs bridging experimental and Density Functional Theory (DFT) simulation of materials
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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