Miriam Rodriguez Carrasco, M.Sc., is from Mexico City, where she studied Physics at the National Autonomous University of Mexico (UNAM). Her thesis focused on the effects of climate change on rainfed agriculture. She later earned a Master's degree in Applied and Environmental Geoscience from the University of Tübingen, with a concentration in Environmental Physics. She wrote her thesis about machine learning methods for ENSO forecasting. Her current research interests as a PhD candidate are how to develop climate resilience in land systems by combining satellite data, ecosystem modeling, and strategies for detecting climatic extremes. She is particularly interested in novel techniques for evaluating the effects of climate change on agricultural and land systems.

Miriam joined the group of researchers of the Green Research Network at the Centre for Climate Resilience at the University of Augsburg. She is working on the sub-project "Stabilizing natural and managed land systems against climate disturbances and extreme events - DETECT."

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César Alvarez is a geospatial engineer and expert in geoinformatics, GIS, remote sensing and drones. He also focuses on data science, integrating various environmental, climate, and agricultural variables from geospatial sources. Cesar is from Ecuador and acquired his degree in geospatial engineering from the Army Polytechnic School (ESPE) in Ecuador and later obtained a? master's in environmental management from the same institution. He also holds a doctoral degree in surveying engineering from the University of Porto, Portugal, where he focused on Remote Sensing Applications in Environmental Sciences using machine learning models. Cesar has over 11 years of experience as an assistant professor at the Salesian Polytechnic University in Ecuador, where he has been involved in lecturing and research related to geospatial sciences. Cesar also worked as an international geospatial consultant on projects for CIAT and ICARDA.

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Martin Dautriche is from Vannes in France, where he studied for a bachelor's in computer science at the University of South Brittany with a focus on software engineering and machine learning. He later earned a double master's degree in geodata science, remote sensing, and earth observation from the University of Salzburg and the University of South Brittany with a concentration on machine learning and deep learning applied for GeoHumanitarian action and disaster management. His master's thesis was "Transfer of automated building characterization model for seismic risk assessment using universal domain adaptation." His research interests are, applying spatial simulation models and machine and deep learning algorithms to study the effects of climate change on conflict, population migration, human ecosystems, and the economy. Martin will mostly be working remotely but will occasionally visit the centre and Augsburg.

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