Doctoral Students
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Our doctoral students conduct research and are guided and trained to succeed in their further academic career. As Agent of Changes they will later contribute to the long-term success of the network. |
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Julio Joaquín Álvarez Estrella |
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Doctoral Candidate in SDG Nexus Network Justus Liebig University | Giessen, Germany |
Doctoral Candidate in SDG Nexus Network Justus Liebig University | Giessen, Germany Digital agriculture, sustainability, soil systems, food security, remote sensing, and precision agriculture |
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Douglas A. Gómez-Latorre |
William Alfonso Leon Rueda |
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Doctoral student in Agricultural Sciences - Soils and Water Universidad Nacional de Colombia | Bogotá, Colombia Agroclimatology with emphasis on the analysis of agroclimatic risk, |
Doctoral student National University of Colombia | Bogotá, Colombia Climate change, resilience and risk assessment, spatial modeling and remote sensing applications, and watershed-based environmental analysis. |
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Adriana Priscila Sumba Naula |
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Doctoral Candidate in SDG Nexus Network Justus Liebig University | Giessen, Germany Machine/Deep Learning applications in environmental remote sensing |
Doctoral Candidate in SDG Nexus Network Justus Liebig University | Giessen, Germany
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Nagima Shukurbekova |
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Doctoral Candidate National University of Colombia | Bogota, Colombia Land use monitoring and management, land use change models, and territorial digital twins |
Institute of Public Policy and Administration (IPPA) University of Central Asia | Bishkek, Kyrgyzstan Agricultural economics, rural development, climate change, and gender equality |
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 Photo: Privat |
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Munguntuul Ulziibaatar |
Mateo Vélez-Hernández
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Doctoral Candidate in SDG Nexus Network Justus Liebig University | Giessen, Germany Environmental and natural resources management, Rangeland study, Climate change, Socio-economic studies, Land use and land cover change |
Doctoral Candidate in the SDG Nexus Network
Water Resources and Environmental Sciences
University of Cuenca | Cuenca, Ecuador
Hydrological hybrid modelling for Energy Security. Deep learning, distributed models, and remote sensing to reduce uncertainty in precipitation and runoff modelling.
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