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programming (e.g., R, Python). Strong record of peer-reviewed publications in plant science or related fields. Excellent communication skills and ability to work in a collaborative, interdisciplinary team
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in languages like Python or R. Professional experience in the application of Machine Learning algorithms in the mapping and correction of spatial data. Professional experience in data analysis
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gene identification (MEGARes, CARD, ResFinder). Proficiency in Python, R, or Perl, with experience in Linux/Unix environments. Solid understanding of antimicrobial resistance mechanisms, horizontal gene
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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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software (e.g., R, SAS, SPSS) and data modeling tools. Knowledge of integrated soil fertility management and sustainable agricultural practices. Publication record: A good track record of publishing research
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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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desirable. 3. Technical skills: Proficiency in soil and plant analysis techniques (e.g., nutrient quantification, soil microbial assays). Experience with statistical software (e.g., R, SAS, SPSS) and data
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world leader in fertilizer production, is a major starter client for the University providing capital and research funds. In parallel, a new portfolio of clients is growing with the development of an R&D
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a PhD in synthetic organic chemistry and must have: Clear understanding of the scope and role of organic chemistry in R&D Experience in handling hazardous reagents (solvents, chemicals…) Advanced
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parallel, a new portfolio of clients is growing with the development of an R&D cluster around the University and a growing number of international partnerships. Applications and selection procedure