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, Digital Soil Mapping, Remote sensing (COPERNICUS data ecosystem), spatial data modelling, spatial analysis, neural networks, large scale datasets management with GIS, cloud computing, Big Data tools. You
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interdisciplinary research group dedicated to developing integrated approaches to geospatial systems analysis. Our team pushes the boundaries of how spatial data can be used to tackle today’s pressing environmental
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deep learning models (e.g., adapting methods in [6]) based on spatial cellular graphs constructed from these images to predict clinical outcomes. The research will be carried out using two
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the molten salt NaAlCl4 electrolyte chemistry by among others in situ Raman probe spectroscopy to investigate spatially resolved compositional changes during charging/discharging of the battery, and
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. These data will be integrated into TemisFlow (Themis) thermal models to reconstruct the thermal and subsidence history of the basins. The modeling will quantify the distribution of heat flow during
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approaches. The PhD will develop and apply optimization-based energy system models to analyse whether spatially coherent urban and energy configurations can be operated efficiently under realistic physical
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uses cutting-edge techniques including single-cell and spatial transcriptomics, proteomics, super-resolution microscopy, in vivo tracking, mouse models, and human patient tissues and iPS-derived cells
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applications, agricultural monitoring and modeling, Agro-AI/ML, or digital twin. Instructions to Applicants: For full consideration, applicants must apply for the Research Assistant Professor at https
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funding. Appointment Start Date: Fall 2025 Group or Departmental Website: https://hph.stanford.edu/careers/ (link is external) How to Submit Application Materials: Submit all application materials
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to demonstrate knowledge of computational epidemiology, individua lbased simulation, spatial modeling of epidemics and other geospatial software. * Ability to show proficiency in data management. * Ability