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will use a combination of scRNAseq, spatial transcriptomics, and highly-multiplexed imaging to understand how human macrophages respond to the early stages of cancer development. They will be a part of
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and aggression, using optogenetics, in vivo imaging, electrophysiology, and sophisticated machine learning/artificial intelligence analyses of mouse behavior. All projects have translational components
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Cancer Registry, as part of the national SEER registries. The postdoc fellow will work closely with statisticians, computer scientists, oncologists, and epidemiologists in the lab and other collaborating
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the Laboratory can be found at https://www.nivs-lab.org/ (link is external) . Required Qualifications: Ph.D. in Biomedical Imaging, Neurobiology, Bioengineering, Medical Physics, or related areas Be
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the United States as well as clinical imaging and testing data from Stanford. Project themes will include developing models using EHR data to predict outcomes in ophthalmology and glaucoma, as well as investigating
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model APIs, cloud computing environments, and R for additional statistical analysis. For decision support prototype development and evaluation, web-based user interface design, human-computer interaction
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whole-body cancer detection. We are interested in both technical development and clinical translation pipelines, leveraging resources at the Lucas Center for Imaging, synergies across the clinical MRI
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fMRI, task fMRI, and multi-band functional imaging. A clear motivation to pursue research in neuroimaging that is applied to understand and treat brain circuit disruptions. A demonstrated capacity to
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Program at the Stanford Cancer Institute. She has an academic interest in Precision Medicine and her lab applies cutting-edge sequencing and imaging technologies to better understand skin cancer and rare
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quality control procedures and processes). May follow up with Institutional Review Board (IRB) to ensure renewals are approved and completed, seeking guidance where necessary. Assist with development