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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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community; support multi‑omics data integration and analysis across multiple research groups; and collaborate on the development and maintenance of computational pipelines for spatially resolved
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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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. 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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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
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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 study chromatin and gene regulation in mammalian cells and human disease systems. Current ongoing projects include: statistical modeling and advanced machine learning/AI method development for predicting
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) and induced pluripotent stem cells (iPSc) as model systems, as well as human brain tissue. In the future, and as our research program advances, we will expand our toolkit to also include mouse work and
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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