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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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, transportation systems and biological interactions. These systems are represented as networks. A network is a set of objects that are connected to each other in some fashion. Mathematically, a network is
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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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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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2 Sep 2025 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Computer science Mathematics Researcher Profile Recognised Researcher (R2) Established Researcher
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21 Aug 2025 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Computer science Mathematics Researcher Profile Recognised Researcher (R2) Established Researcher
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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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2 Sep 2025 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Agricultural sciences Biological sciences Mathematics Researcher Profile Recognised Researcher (R2
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of the extraction and beneficiation system. This work will require an understanding of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict
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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