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Field
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precision cardiovascular medicine, leveraging cutting-edge approaches such as human iPSC-derived 2D cardiovascular cells, 3D vascularized cardioids, CRISPR gene editing, high-throughput CRISPRko/i/a screening
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static and dynamic 3D reconstruction, semantic scene understanding, and generative models for photo-realistic image / video synthesis. Overall, the main focus is on high-impact research with the aim
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for such models include high-resolution 3D imaging, time-resolved materials characterization, and atomic structure determination. Scientific instrument data is often multimodal in nature and developing DL models
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approaches for important questions in neuroscience. We have multiple current and incoming NIH projects to establish cellular cell type architecture maps of mammalian brains using mice as an animal model. Three
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(PET), functional MRI (fMRI), and electroencephalography (EEG), using advanced quantitative modeling techniques and artificial intelligence methodologies in brain diseases. The candidate will work
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a unique opportunity to work in a cutting-edge, interdisciplinary environment, leveraging a novel in-vitro model of the human uterus and/or cutting edges machine learning techniques to make
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models, 3D co-culture of cancer cells with other compartments of the tumor microenvironment, organoids and/or patient-derived organoids models. Fluorescence microscopy and immunohistochemistry, multiplex
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Post-Doctoral Associate in the Center for Translational Medical Devices (Modeling) - Dr. Rafael Song
, cardiovascular, and neurologic disease. These projects entail computational modeling, device design and manufacturing, optimization of chemical, mechanical, and electrical characteristics, and preclinical
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robust 3D imaging technique suitable across diverse scientific communities. This project has two interconnected aims. The first is to create an accurate “digital twin” of the experiment through end-to-end
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at the intersection of systems neuroscience and computational modeling. Our lab is broadly interested in Bayesian inference, perception, multisensory integration, spatial navigation, sensorimotor loops, embodied