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Qualifications Demonstrated experience analyzing large-scale epidemiologic datasets and/or clinical trial data. Formal training or research experience in sleep research or related disciplines (i.e. sleep medicine
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as
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the ability and motivation to develop expertise in large-scale model training and scaling on HPC systems, as well as in handling the unique characteristics of scientific data, including large-scale numerical
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working in large teams with diverse disciplinary, sectoral, and cultural backgrounds; o For those experts in qualitative data, qualitative data experience and analytical skills are considered an asset. Duke
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motivated Postdoctoral Research Fellow to develop and apply innovative, data-driven models for seismology, with a focus on developing cutting-edge foundation models. This is an exceptional opportunity
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, or related fields and the successful applicant must have advanced quantitative and biological skills and an understanding of basic disease dynamics. The work will explore large datasets on bat viruses we have
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transcriptomic data. • Detect and interpret structural variation from Nanopore/PacBio sequencing. • Build scalable, reproducible pipelines for large genome collections and public databases. • Collaborate closely
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PhD with an interest in computational social science or data science (broadly defined). This individual will help design, execute, and analyze a series of field experiments on a platform created by the
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(circRNAs) are associated with Alzheimer’s disease status and when measuring in blood can be used as disease biomarkers. We have generated brain and blood transcriptomic data in thousands of samples with
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, and contributing to a robust data infrastructure that makes large-scale, multimodal datasets FAIR and AI-ready. You will publish findings in high-impact journals, present at major international