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questions, advancing deep learning models, or other topics discussed with the PI. We use publicly available and simulated genomic data. Core job duties include: (1) Building computational pipelines
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tissue-specific mutant mouse models to investigate the underlying mechanisms controlling muscle growth and metabolic flux. The projects are multidisciplinary with excellent opportunities for advanced
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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven
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tissue-specific mutant mouse models to investigate the underlying mechanisms controlling muscle growth and metabolic flux. The projects are multidisciplinary with excellent opportunities for advanced
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model. The overarching goal for this position is to make timely progress on our NIH grant entitled “Mechanisms of pterin-dependent regulation in proteobacterial systems” (Grant number R01 GM160733) which
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using proteomics. (3) determine differences between DsbA proteins of two model organisms, E. coli and M. smegmatis (4) Establishing termini restraining for inhibitor-protein crystallography in
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conduct research related to creating or testing deep learning models for genomics, exploring new techniques related to spatial simulations, or other topics discussed with the PI. Basic Qualifications Core
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management. Artificial Intelligence & Machine Learning: Predictive risk modeling for falls, hospitalization, and cognitive decline; Natural Language Processing (NLP) for remote cognitive assessment; image
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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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Responsibilities Pharmacological Modeling: Utilize computational tools to predict potential drug-drug interactions, metabolic pathways, and off-target toxicities within complex physiological systems. Team Science