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related field. Strong track record in remote-sensing imagery and/or time-series analysis and ML/DL for spatio-temporal data. Advanced Python skills and experience with ML frameworks and geospatial tools
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CSAES - Postdoctoral in Experimentation and analysis of the dissolution and diffusion of different fertilizer formulations About UM6P: At the heart of the future Green City of Benguerir
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or defended before start) in Computer Science, Remote-Sensing/Geoinformatics, Agricultural Data Science, or related field. Strong track record in remote-sensing imagery and/or time-series analysis and ML/DL
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analysis (TEA). The successful candidate will: Develop innovative strategies for transforming industrial byproducts into high-performance porous adsorbent materials. Design and optimize structuring and
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Challenges Addressed in the Position: Heterogeneity and high dimensionality of multi-omics data requiring advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing
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team. Identification of the isolated strains using 16s RNA and ITS Conduct the whole-genome sequencing experiment of the selected strains. Conduct taxonomy characterization and analysis of selected
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of vanadium-bearing materials to understand their transformation and recovery mechanisms. Evaluating environmental sustainability via Life Cycle Assessment (LCA) and techno-economic analysis. Contributing
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of computational tools for process monitoring and predictive analysis Candidate Criteria Applicants should have: A PhD in Process Engineering, Chemical Engineering, or a closely related field Strong expertise in
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digital twins for industrial chemical processes Process optimization and model-based decision support tools Development of computational tools for process monitoring and predictive analysis Candidate
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propose evidence-based corrective measures or process improvements. Perform detailed data analysis and interpretation, and prepare high-quality technical reports, presentations, and documentation