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-doctoral fellowship in person in São Paulo, Brazil. Fluency in English. Basic knowledge of statistics and willingness to learn how to conduct systematic reviews and meta-analyses. Previous experience in
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cities and databases. Mandatory requirements: PhD in engineering, data science and computing, mathematics, or statistics; experience in engineering, data science and computing, mathematics, or statistics
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strategies and artificial intelligence based on chemical analysis results. The researcher will be responsible for the integration, processing, and interpretation of data, applying statistical methods and
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field assessments; experience in dataset organization, statistical analysis, and/or soil health assessment frameworks; previous publication(s) of scientific paper(s) on soil macrofauna; familiarity with
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cultivation under different experimental conditions. Experience with chromatographic techniques, particularly HPLC and/or LC-MS. Experimental design and statistical analysis. International scientific
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, statistical analyses (including multivariate approaches), and scientific writing. Mandatory requirements: PhD in physiology, ecology, neuroendocrinology, molecular biology, or related fields. Strong experience
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management databases; applying statistical methods to identify the main factors driving yield gaps; estimating productivity frontiers and crop responses to management practices; developing agronomic
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, functional, and nutritional characterization, and their application in diverse food systems. The fellow will also contribute to the design, execution, and optimization of experiments using statistical
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experience in image-based phenotyping, artificial intelligence applied to grapevine breeding, genome-wide association studies (GWAS), genomic selection (GS), and statistical analysis. Mandatory requirements
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; • Familiarity with statistical analyses and modeling as well as machine learning approaches, supported with strong skills in computational optimization of methods; • Experience working with large-scale datasets