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to evaluate and inform digital health interventions for women at increased risk of GDM. The project will primarily utilize data collected from a completed randomized controlled trial (https://bump2babyandme.org
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to evaluate and inform digital health interventions for women at increased risk of GDM. The project will primarily utilize data collected from a completed randomized controlled trial (https://bump2babyandme.org
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such as Imaris or similar) is required. Experience with proximity ligation assays and CRISPR/Cas9 is highly desirable. Experience with single-cell RNA sequencing and downstream data analysis is highly
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the analysis of linked health registries though the development, implementation and application of methods for causal inference in time-to-event outcomes. For more information and how to apply: https
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). Theoretically, the project draws on the existing literature about the gendering of fisheries, in dialogue with gender studies, social science concept analysis, legal anthropology and anthropological approaches
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experiences and skills will be emphasized: Experience in plant phenotyping, drought stress research, or root biology Familiarity with imaging technologies, statistical analysis, or quantitative data analysis
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members and more than 55 doctoral students and postdoctoral fellows. The Department conducts research at an international high level within the disciplines of algebra, analysis, didactics of mathematics
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responsibilities (dependent on experience level of applicant) Develop and improve multi-temporal InSAR processing algorithms (e.g., time series analysis, phase unwrapping, noise mitigation, filtering, atmospheric
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of GNSS-R and other signals of opportunity. Tasks will be literature reviews, simulations, data analysis from similar missions and/or data from project partners. The research will also include planning and
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distributions, involving both in-situ studies of evolving solidification microstructures and ex-situ studies of the final as-cast components. Developing and applying high-throughput data analysis methods