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development – particularly in how longitudinal study designs contribute to our understanding of human development and the psychological changes processes. The lab develops and uses novel longitudinal methods
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or involved in RAPID surveys. This project constitutes a critical component of the New Ecology of Early Childhood (link is external) being developed at SCEC. Key Responsibilities: Conduct statistical analyses
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motivated postdoctoral researcher with extensive experience with item response theory models, computer adaptive testing, and related measurement methods. Demonstrated ability to bridge research (innovative
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methods to improve prediction model generalizability, model fairness, and generalizability of fairness across different clinical sites. The researcher will have the opportunity to use machine learning and
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implementing novel strategies for an Adult Editing System, establishing robust Cas9/gRNA delivery methods for somatic cells in adult organisms. You will collaborate closely with a dynamic, multi-disciplinary
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substitution in the EGFRvIII peptide significantly increases survival in an animal model of glioblastoma by enhancing proteasomal processing. We also developed robust methods to detect a new class of non
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personalized learning experiences that are both effective and engaging. The first component of this work is to develop AI-augmented tools that enable elementary school-aged children to rapidly create
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of application a track record of successfully completing several quantitative methods or mixed method graduate courses a dissertation or published manuscript that relies on a quantitative or mixed-method approach
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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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from real world longitudinal data on management and health outcomes for children with mental health conditions. Methods have included deep learning, large language models (LLM), generative AI models (Gen