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, observations, a hierarchy of numerical models, and machine-learning methods to understand their formation, dynamics, and predictability. The successful candidate will have substantial freedom to develop
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methods to well-annotated clinical cohorts to address questions in early detection, minimal residual disease, treatment response prediction, and resistance characterization. Our goal is to bridge technical
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and AI-based approaches, including predictive modeling, stratification, explainable AI, and integrative multimodal analysis. The scholar will have opportunities to lead first-author publications
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on physiologic waveform analysis, biomedical signal processing, and computational modeling of continuous clinical monitoring data. The successful candidate will work on projects involving the analysis
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paired with computational biology and machine learning to develop predictive AI models of how cells interpret and respond to the surrounding extracellular matrix. Required Qualifications: We are looking
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intelligence as applied to trauma systems and acute care surgery. Fellows will engage in cutting-edge research spanning multiple domains, including risk prediction models for surgical complications, clinical
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physiological responses to hormones, neurotransmitters, and environmental stimulants. We take an interdisciplinary approach that combines proximity labeling proteomics, structural biology, computational modeling
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them to develop prediction models for patient safety events. In addition to priority projects, the Postdoc will have the opportunity to work with other researchers both at Stanford and within our larger
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informatics is desirable. Required Qualifications: Ph.D. with a strong background in biomedical informatics (e.g. information extraction from electronic medical records and predictive modeling) Proven track
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campus as arts and sciences. The fellow will lead the development and validation of imaging-based models to predict patient response to cancer treatment (80%) and will manage the unit’s AI/ML core