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the fundamental factors governing cation mobility in aluminosilicate materials through an integrated experimental-theoretical-computational approach. The research will probe the spatial extent and timescales of ion
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microscopy, spatial transcriptomics and in vitro and in vivo models to study brain tumor cell-cell interactions and the organization of the cancer ecosystem. We strive for highly collaborative and inclusive
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captured from UAVs. The research will address the design of AI models capable of combining heterogeneous sensor modalities, including RGB, thermal, LiDAR, acoustic arrays, GPR, and X-ray backscatter
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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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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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, or probabilistic modeling, and be proficient in Python and modern machine-learning frameworks (ideally PyTorch). Experience with single-cell transcriptomics, epigenomics, proteomics, spatial omics, or multimodal
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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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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
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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