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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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field. Strong background in machine learning, particularly deep learning and natural language processing. Experience with transformer-based architectures (e.g., BERT, GPT) is highly desirable. Proficiency
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city models focused on health and environmental infrastructures. Advanced Data Analysis: Advanced skills in machine learning, deep learning, and advanced statistics for processing complex data. Urban
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About UM6P: Mohammed VI Polytechnic University (UM6P) is an internationally oriented institution of higher learning, that is committed to an educational system based on the highest standards
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oriented institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development
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pathway prediction. Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions, and identify novel synthetic routes. Curate and manage reaction datasets from literature
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., NeurIPS, ICML, ACL, EMNLP, etc.). Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding of transformer
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the roles of water and atmospheric physics and chemistry in forming soils (top and deep soils) and how the use of the topsoil for agriculture interacts with geochemical processes. The candidate is expected
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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