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knowledge sharing. Mentor and support trainees, students, and new team members as appropriate. Scholarly Contributions Assist with manuscript development, abstracts, presentations, and grant-related research
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The Human-AI Research Pipeline (HARP), part of Duke University's Society-Centered AI Initiative, is an interdisciplinary research program examining how Artificial Intelligence (AI) affects students' cognition
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, technologies, and commercial reagents, sharing knowledge with team members. Contribute to the development and implementation of new research procedures and innovative experimental approaches. Collaborate
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. Knowledge of survival analysis. Knowledge of machine learning methods. Other Requirements Application materials must include: Curriculum Vitae (CV) Statement of research interests Names of three professional
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. This position is best suited to those who enjoy statistics, coding, manipulating and visualizing data, and other quantitative analysis tasks. Work Performed The new researcher will leverage knowledge and skills
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fields: Agentic AI, Large Language Models, Artificial Intelligence, Biomedical Ontologies, Biomedical Knowledge Graphs, Computational Biology, Bioinformatics, Biomedical Informatics or a related field
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populations. Knowledge of sleep physiology and sleep staging/scoring using established clinical standards. Excellent analytical, organizational, and scientific communication skills. Ability to work
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disease. Through innovative research approaches, you will contribute to discoveries that improve knowledge of retinal biology and inform future therapeutic strategies. What You'll Do Research & Discovery
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. Candidates with background knowledge and hands-on experience in mouse models, proteomics, 3Dorganoids,primary cells purification and culture skills are particularly welcome. Minimum Requirements: Ph.D
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, Medicare/Medicaid claims, PCORnet, Epic Cosmos, Flatiron Health, TriNetX, or similar oncology datasets. Knowledge of survival analysis, longitudinal data analysis, mediation analysis, latent class analysis