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of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict ore quality and optimize operational decisions. 2. Key Responsibilities
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sensor data, public databases, and GIS. Predictive Modeling:Design predictive models to evaluate the impact of urban and environmental policies on public health. Interdisciplinary Collaboration:Collaborate
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. Experience with data prediction and classification techniques. Computer Skills: Good proficiency with optimization tools (CPLEX, SAP). Experience with data analysis software. Soft Skills: Analytical mindset
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under different climatic and social contexts to provide decision-making tools for sustainable urban planning. Main Tasks and Responsibilities: Develop a predictive model integrating the direct and
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fully observed data. These methods can be applied to complete or predict links in a network. However, missing information in a network can include both missing edges and nodes which makes classical matrix
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interfaces, and condensation surfaces under varied operating scenarios. Develop and refine performance simulation models and predictive tools to support system optimization and deployment strategies. Prepare
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system for bacterial genomes using cutting-edge genomic language models. This project aims to adapt and extend transformer-based architectures to create a powerful tool for understanding and predicting
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networks, metabolic networks) to identify key disease drivers and biomarkers. Build predictive models for disease classification, patient stratification, and treatment response prediction. Collaborate with
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
analysis for monitoring disease progression and treatment outcomes. Key Responsibilities: Develop and implement AI/ML pipelines for feature selection, dimensionality reduction, and predictive modeling using
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deep learning techniques to improve image processing and trait prediction. Analyze large datasets generated by the Phenomobile.v2+ to identify key traits affecting crop performance under stress