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Doctoral student - Deep learning in satellite imagery to monitor health indicators in LMICs Created at 8/1/22, 10:53 PM

The PhD student will work under the guidance of Dr. Pascal Geldsetzer and other researchers at both Heidelberg University and Stanford University, including both population health researchers and computer scientists. The researcher will help with expanding the group’s dataset for analysis, as well as building and evaluating convolutional neural networks. The broad aim of this research is to test whether satellite imagery along with other geo-referenced publicly available data (e.g., data from OpenStreetMaps, climate and weather variables, and infrastructure-related variables) could be used to monitor important health indicators in low- and middle-income countries at a highly granular geographic and temporal level. In addition to this core project, the research group works on a wide variety of research, with foci being medication effectiveness, health services research, and population health issues. The PhD student will be expected to publish in high-impact peer-reviewed journals.

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