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scale membrane fabrication and characterization, as well as in the characterization of the physical, chemical, and separation properties of membranes. In-depth knowledge of the properties of materials
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research, particularly in gut microbiota. Experience with next-generation sequencing (NGS) and omics data analysis. Knowledge of microbial ecology, dysbiosis, and host-microbiome interactions. Familiarity
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-cell microbiome analysis, or multi-omics data integration. Knowledge of machine learning approaches for resistome prediction or biomarker discovery is a plus. Why Join Us? Access to cutting-edge
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any related field, and a good knowledge of molecular biology. The candidate will be part of the bioinformatics laboratory at the college of computing (bioinformatics.um6p.ma). The project aims to build
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environments (mining, semi-arid, Saharan). Proficiency in plant functional trait analysis and functional ecology methods. Strong knowledge of soil characterization (physico-chemical analysis, mineralogy, soil
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sequencing (NGS) and omics data analysis. Knowledge of microbial ecology, dysbiosis, and host-microbiome interactions. Familiarity with cell culture techniques. R, Python, or other data science tools
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understanding of the scope and role of organic chemistry in R&D Experience in handling hazardous reagents (solvents, chemicals…) Advanced knowledge in designing and execute experiments, analyze data, and
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and machine learning to optimize treatment conditions. Contribute to the development of reproducible stress priming methods and assist in transferring knowledge to agricultural stakeholders. Required
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in Computational Biology. The candidates should be highly motivated and have a Ph.D. in computational biology or any related field, and a good knowledge of molecular biology. The candidate will be part
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Science, Chemical Processes, or a related engineering discipline. A strong background in electrochemical surface science. Proficiency in elemental and structural analysis using various techniques. Knowledge