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in applied economic analysis (e.g., causal inference, econometrics, spatial equilibrium modeling). • Experience working with large-scale datasets and interdisciplinary research. • Demonstrated research
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Immunology, Data Science and/or related fields. MD/PhD with molecular biology research experience. Must have experience with analyzing omics data. Familiarity or direct experience with analysis of 10x Genomics
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interventions and “nudges” in collaboration with external partners (e.g., companies, nonprofits, policy organizations) • Using advanced data analysis methods to extract interpretable patterns from large, messy
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, or engineering. Preferred Qualifications: The ideal candidate will bring experience or a very strong foundational understanding in areas such as high‑dimensional data analysis and translational research, and must
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with our co-PI at UCLA. Responsibilities include study design, supervising and leading data collection, coding, and analysis, and writing of manuscripts for publication, as well as grant preparation
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interdisciplinary research addressing climate, environment, and/or sustainability challenges. PREFERRED: - Strong quantitative skills in applied economic or financial analysis, including experience with causal
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. quantitative and/or qualitative counterfactual-based approaches, Difference in Difference models, Qualitative Comparative Analysis, Bayesian hierarchical modelling); o Experience working with and synthesizing
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proposal with the study group under the direction of PI. Lead pilot data gathering and analysis for the proposal as needed Adhere to timelines Choose Duke. Join our award-winning team and be part of
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, deep learning Computational genomics, network modeling, spatiotemporal/functional data analysis, time-series Strong programming in R and/or Python; best practices in reproducible research Excellent
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/bioinformatics, and data science. Work Performed · Work in highly collaborative inter-disciplinary environment with clinicians, econometricians, statisticians, and data scientists · Lead statistical analysis