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, biologics, and cannabis. Apply statistical and machine learning approaches (e.g., sequence analysis, latent class analysis, clustering) to examine medication use trajectories and patient subgroups
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the Project: Brain resilience refers to the capacity to resist adverse outcomes despite significant risk factors. In late-onset Alzheimer’s disease (AD), Apolipoprotein E4 (APOE4) is recognized as the
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. This project aims to examine the neurobiological and mental health impacts of cannabis exposure, with a broader goal of identifying biomarkers associated with resilience and risk for depression. As part of
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applications for a postdoctoral fellowship position to join a project investigating trafficking risks in charcoal supply chains in Brazil. The position is open to recent graduates of PhD programs in statistics
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and chronic disease prevention Citizen science for health promotion and disease prevention Strategies to improve clinical delivery of prevention and chronic disease management Chronic disease risk
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limited to (1) electronic health records, (2) causal inference, (3) natural language processing and large language models, (4) generative AI, (5) dynamic risk prediction modeling for high-dimensional
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paradigms in primates or humans – Theoretical neuroscience, machine learning, or AI • Proficiency in Python, MATLAB, or equivalent data‑analysis frameworks. • A passion for big‑picture questions, open science
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) cancer screening modeling, (2) microsimulation, (3) decision analysis/health policy modeling, (4) survival data under competing risks, (5) dynamic risk prediction modeling (e.g., landmark model), and (6