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cell-derived embryo models would be desirable. Experience with advanced imaging techniques. Experience with single-cell transcriptomics. Familiarity with multi-omics techniques, spatial analysis
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environmental stimulants. We employ an interdisciplinary approach to probe, model, and predict how signaling network dynamics translate extracellular cues sensed by GPCRs into specific phenotypic outputs
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employ advanced analytical methods in large databases, which include claims data and electronic health record data in conventional structures and in common data models. Our research group prioritizes a
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are seeking a highly motivated Postdoctoral Research Fellow with expertise in large language models (LLMs) and electronic phenotyping to join our dynamic team focused on advancing cancer research through
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the mechanism of membrane protein biogenesis and quality control at the human endoplasmic reticulum using a combination of cutting-edge functional genomics, cell biology, structural biochemistry and
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understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. Responsibilities: Conduct research on the mechanisms underlying
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of the selected candidate, budget availability, and internal equity. Pay Range: $80,000-95,000 The Alsentzer Lab at Stanford is seeking a postdoctoral fellow to advance trustworthy, deployable AI methods
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actions on their behalf. This project aims to advance the design and implementation of AI agents that prioritize user autonomy, consent, and benefit, while also delivering real-world impact and contributing
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external) How to Submit Application Materials: To begin the application process, please send an email using the subject line “Postdoctoral Position in Machine Learning for Advancing Mental Health” to Tina
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of physicists, engineers, and oceanographers interested in advancing our understanding of ocean science, water sustainability, and climate resilience. Our lab develops optical techniques based on spectroscopy