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convolutional-neural-network architecture for crop classification. Applied Sciences, 11(9), 4292. Bhattacharya, S. & Pandey, M. (2024). PCFRIMDS: Smart Next-Generation Approach for Precision Crop and Fertilizer
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-Guachi, L., Gomez-Mendoza, J.B., Revelo-Fuelagan, J. & Peluffo-Ordonez, D.H. (2021). Enhanced convolutional-neural-network architecture for crop classification. Applied Sciences, 11(9), 4292. Bhattacharya
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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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experience in natural product extraction, purification, and metabolite identification. Proficiency in the use of LC-MS/MS, GC-MS/MS, NMR, and relevant data analysis software (e.g., MestReNova, Thermo
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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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(CCDC, RCSR, ...) and softwares (Material Studio, Diamond, Origin, ....); Evidence of experience in adsorptive or/and membrane separation technologies. Applications must be submitted online, before 1