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. Strong programming skills 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
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, environmental sciences, geoinformatics, or a related discipline. Proficiency in advanced learning techniques and statistical modeling. Strong programming skills in languages like Python or R. Professional
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techniques (e.g., nutrient quantification, soil microbial assays). Experience with statistical software (e.g., R, SAS, SPSS) and data modeling tools. Knowledge of integrated soil fertility management and
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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