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Nursing (MSN), Doctor of Nursing Practice (DNP), and PhD in Nursing. Our programs are designed to meet the evolving demands of today’s healthcare landscape, equipping graduates with rigorous training
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computational social science, including data capture, cleaning, analysis, machine learning, NLP, and database administration. OR equivalent combination of relevant education and experience. Preferred
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administration). Assist with following "lights on" procedures to verify the integrity of collected data and complete the data collection process. (e.g. learn to perform the physiological and instrument
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knowledge to real-world circumstances. Candidates must have a PhD, J.D. or other terminal degree in their academic discipline to be able to teach a graduate-level course. Candidates with an MA plus 20 years
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related field • Strong quantitative background (e.g. ecological theory and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated
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accelerators including TrueBeam machines – all with onboard kV/MV radiograph/CBCT for IGRT and gated treatment, Halcyon, Ethos, large-bore CT simulators, PET/CT, MR unit, HDR units, ARIA information system, and
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contributed to the identification of vaccine and therapeutic candidates currently being produced in Good Manufacturing Practice facilities for early phase clinical trials. Minimum Requirements: PhD in
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for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell
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renewal. The Fellow will be expected to be in residence, to conduct research in Duke's library and archival collections, to participate actively in the intellectual life of the university, to teach up to 2
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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental