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is a plus. Candidates should also demonstrate strong skills in Python (for ML/NLP tasks) and R (for statistical modeling or data analysis), as both will be actively used in the research workflow
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translational oncology, and a vibrant research community that spans discovery to clinical implementation. Specific Responsibilities include: experimental design, data acquisition, data processing, statistical
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for individual-, longitudinal-, and group-level inference Applying advanced statistical models for longitudinal data (e.g., mixed-effects models, missing-data handling) Integrating imaging outputs with behavioral
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Qualifications: PhD with substantial expertise in data science, geospatial techniques, and statistical/causal inference Required Application Materials: CV 1-page cover letter describing research background and
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strong background in applied statistics, but lack experience addressing policy-relevant questions, analyzing large datasets, or working in applied settings. Applicants from non-traditional backgrounds
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graduates of PhD programs in statistics, economics, computer science, operations research, or related data science fields. The position provides opportunities to participate in rigorous, quantitative research
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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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, robust, and reproducible data analysis. Conventional statistical approaches will be combined with innovations in interpretable machine learning to address each aim from multiple angles. Analysis code will
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preferred. Profound experience in statistical and computational approaches. Proficiency in scientific programming (e.g., R or Python) and scripting in research environments is required. Substantial experience