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Field
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exceptional postdoctoral research fellows interested in developing deep learning and computational methods for pathology image analysis, multimodal data integration, and other medical modalities (e.g
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translatable novel cell-targeted nanotechnologies for target-specific drug/gene delivery and imaging applications to help diagnose and treat unmet medical problems in the areas of brain and eye diseases, and
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model development. -Proficiency in molecular and cellular biology techniques (e.g., DNA/RNA extraction, flow cytometry, histology, fluorescence imaging). -Proven record of first-author publications in
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. The Sharma lab is developing rationally designed, smart, clinically translatable novel cell-targeted nanotechnologies for target-specific drug/gene delivery and imaging applications to help diagnose and
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the development of new radiopharmaceuticals, radiochemistry methodology and translation of lead agents for biomedical imaging. The incumbent will also participate in ongoing radiochemistry procedures for current
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projects that apply machine learning and advanced computational modeling to integrate multi-omics, clinical, and imaging data for biomarker discovery and mechanistic insights in AD. The position offers
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system using mouse models. Techniques may include confocal imaging, brain slice electrophysiology, in vivo manipulations, molecular biology, and behavioral analysis. Genetic mouse models will be applied
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diseases. Our laboratory is equipped with state-of-the-art instrumentation for the proposed studies and have generous access to a Titan Krios G3i Cryo-TEM equipped with a K3 direct electron detector, imaging
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regenerative medicine in the lab of Dr. Kai Mesa at Princeton University. The Mesa Lab has pioneered innovative intravital multiphoton imaging techniques, paired with advanced mouse genetic tools, to observe and
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the relationship between acoustics backscatter, optical backscatter, and optical imaging, with a focus on data collected with in-situ AUV systems and airborne, water-penetrating LiDAR systems. Specifically