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. The objective of the research is to use an integrated approach combining numerical and analytical techniques, simulations and analysis of available experimental data to study and provide efficient
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geospatial data analysis techniques to large Earth Observation datasets. • Produce high-quality scientific publications in peer-reviewed journals and present research results at international conferences
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advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning
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geospatial modeling approaches to analyze urban environmental processes. • Apply machine learning and geospatial data analysis techniques to large Earth Observation datasets. • Produce high-quality
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-series analysis, and environmental data analysis. • Demonstrated experience in artificial intelligence, including machine learning and deep learning, applied to hydrological or water-related systems
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hydrological modelling, time-series analysis, and environmental data analysis. • Demonstrated experience in artificial intelligence, including machine learning and deep learning, applied to hydrological
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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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functional analysis of soil biology. Utilize high-throughput sequencing technologies to analyze soil microbiomes and interpret the data using bioinformatics tools. Collaborate with interdisciplinary teams
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and develop recommendations. Area of specialization: soil microbiome analysis, general microbiology, Molecular Biology, Agronomy and, Sequence dat analysis and R coding skills Main responsibilities
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methods of soil characterization, monitoring and management, Organization of field campaigns, data collection and lab work, Spectral data analysis, data processing, and model development, ‘R’, Python