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using machine learning and deep learning techniques to generate indicators that allow remote monitoring of restoration. Knowledge of remote sensing (e.g. GEDI, LiDAR, multispectral) and programming (e.g
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technical writing and presentation skills; - Advanced programming skills (Python, JavaScript, R), including image processing, machine learning, and scientific & data visualization libraries; - Strong
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-of-the-art machine learning techniques, such as Deep Learning, LLM, Generative AI, and Graph-based Learning, capable of learning from different data sources and integrating information from various formats and
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resource management; (3) applying machine learning to spatial data analysis; (4) evaluation of agronomic variables such as leaf area index and nutrient dynamics; (5) validation of models and analysis
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planting density and resource allocation; (3) applying machine learning for spatial data analysis; (4) evaluating agronomic variables such as leaf area index and nutrient dynamics; and (5) validating models