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Field
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funding. Develop and apply analytical pipelines integrating high-throughput proteomic data with metabolomic, genomic, and clinical datasets from large prospective cohort studies to identify biological
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Center for Astrophysics | Harvard & Smithsonian | Cambridge, Massachusetts | United States | about 23 hours ago
or less of equivalent full-time post-PhD research experience at the time of the fellowship start date of September 1, 2027. If the PhD defense date (or acceptance of PhD thesis) and the official PhD award
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-performance computing and big data analytics PhD must have been received within the last three years. Preferred Qualifications: Demonstrated understanding of model validation, clinical trial design, and causal
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/cold environments is an advantage. Previous experience in automated processing of large data volumes such as Earth observation data is beneficial. It is an advantage if potential previous experience with
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. These demonstrators will have strong potential for high-profile publications, commercialisation, and/or dissemination as open-source designs. The ideal candidate might be from an academic (e.g. PhD/Postdoc in a related
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implementation science frameworks to guide selection of measures, analytic plans, and interpretation of implementation determinants and outcomes. • Support or lead “big data” analyses
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studies, and how information about benefits, harms, and uncertainty is communicated to clinicians, patients, and payers. We welcome applications from recent PhD graduates and postdoctoral fellows who
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of social dynamics. Motivating aims include the discovery and analysis of hidden large-scale patterns in data/models, elucidating mechanisms underlying the emergence of these large-scale phenomena and their
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 3 months ago
, biomedical engineering, computer science, epidemiology, or a related quantitative discipline. Demonstrated experience with large-scale human genetic data analysis (GWAS pipelines, QC, mixed models, population
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data, field or lab experiments, text analytics, use of AI for big data). Output orientation: Demonstrated capacity to publish in top-tier scientific journals. Ideal (but not required) is experience with