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neurodegeneration and neuroinflammation. This work leverages cutting-edge molecular, cellular, and animal model approaches with direct translational relevance to FTD through work with patient samples. Primary Duties
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Informatics, Health Data Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated
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which the numerous degrees of freedom of light are fully leveraged to enhance imaging performances beyond the limits of classical approaches. Our research group is forging new frontiers in optical
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amplitude analysis and scattering theory, heavy-ion collisions and the properties of hot QCD matter, hadronic probes of beyond-the-Standard-Model physics, and formal developments in lattice QCD. A Ph.D. in
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modeling, hydrological models, and/or climate models. While this topic is the primary focus of the position, the fellow will be encouraged to pursue novel research topics. The successful candidate will join
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fellow to join the 3D Stem Cell Biology Research Lab (https://www.hashinolab.com ) and study normal and pathological development of the human inner ear using stem cell-derived organoids as a model system
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pathophysiology of the inner ear using multiple genetic mouse models and a novel human stem cell-derived 3D inner ear organoid model. We study genetically-mediated hair cell and neuronal degeneration in these model
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: navigation, memory, exploration, or the entorhinal-hippocampal circuit. Prior experience with rodent memory tasks. Experience with computational modeling. To Apply: Interested individuals should apply at https
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strategies in preclinical models (Fang Y, et al., JCI 2021, Zhou Z., et al., Nature BME 2021, Tao Y, et al., Adv Sci 2024). Dr. Zhang’s lab has developed a number of technical platforms that facilitate
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architecture design, simulations, and publicly available genomic datasets to develop new inference methods. The Postdoctoral Associate will conduct research related to creating or testing deep learning models