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economists, physicians, and statisticians—on projects that bridge methods, policy, and practice. Position Description: This role will involve intensive analysis of claims data and related large administrative
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—are intended to foster the early career development of researchers who have transitioned or are transitioning from training environments in the physical/mathematical/computational sciences or engineering into
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Science, Computer Science, Applied Mathematics, Engineering and Physics. Additional Qualifications Expertise (or desire to work) in reduced order modeling, Causal inference and High Performance Computing
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economists, physicians, and statisticians—on projects that bridge methods, policy, and practice. Position Description: This role will involve intensive analysis of claims data and related large administrative
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for analysis of large-scale whole genome genetic and genomic and phenotype data. Examples include large Whole Genome Sequencing association studies, biobanks, single-cell and CRISPR multiome data, integrative
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will work with Prof. Daniel Eisenstein and collaborators on the analysis and interpretation of JWST data, with particular emphasis on deep-field observations. The position provides access to large, high
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possible. Basic Qualifications PhD in computer science, statistics, electrical engineering, applied mathematics, computational biology, or a related quantitative field required by the expected start date
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their microscopic world. We study how microbes adapt to their ever-changing environment, using simple and complex survival strategies. We hope to deeply understand how bacteria react and respond to stress and use
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for candidates with interests in multiscale simulations of complex physical phenomena, from the atomistic/electronic scale to mesocopics and beyond. Of particular interest is the development and application
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for the identification, estimation, transportability, and generalization of the causal effects in complex real-world settings. Among others, methodological areas will span: ● Causal inference for spatiotemporal data