65 experience-design-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" Postdoctoral positions at Duke University in United States
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: Experience in translational or mechanistic cancer research. Experience in experimental design and interpretation informed by clinical questions. Experience in scientific writing, grant preparation, and
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or student contexts. ● Experience with longitudinal research methods. ● Experience analyzing natural language logs. ● Experience with participatory research, co-design, student engagement, or community-engaged
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curiosity, teamwork, innovation, and continuous learning while contributing to groundbreaking research initiatives at one of the nation's leading academic medical centers. Be Bold. Join a team at the
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(Microbiology & Immunology, University of Michigan) Minimum Requirements Ph.D. in applied mathematics, computational biology, quantitative biology, or a related field. Experience in ABM development and ODE/PDE
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, and metabolomics. Design, execute, troubleshoot, optimize, and document experiments with thorough attention to reproducibility and scientific rigor. Compile, interpret, and communicate research findings
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, innovation, creativity, and belonging. Our collective success depends on the robust exchange of ideas—an exchange that is best when the rich diversity of our perspectives, backgrounds, and experiences
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. Minimum Requirements: PhD or equivalent doctorate, such as an MD, or documented completion of all degree requirements consistent with Duke’s postdoctoral policy. Preferred Qualifications: Experience in
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a Postdoctoral Associate or Research Scientist (depending on level of experience) in marine spatial ecology for immediate hire to model species distributions of marine mammals. Directed by Dr. Patrick
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Postdoctoral Associate, Computational Behavioral Analysis & Digital Autism Research Be You. At Duke, we celebrate individuality, innovation, and the unique perspectives that each member of our
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Bayesian meta-analyses. This position also provides opportunities to develop innovative statistical methods related to clinical trial design, variable selection in high-dimensional data, prediction