2,250 computer-programmer-"https:"-"FEMTO-ST"-"U" "https:" "https:" "https:" "https:" "UNIV" positions at Duke University
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care programs Generous retirement contributions Family-friendly policies and resources Cultural and wellness programs Learn more at: https://hr.duke.edu/benefits Ready to Make a Difference? Apply now and
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key policy influencers, organizations, and forums in the DC area relevant to global health and HIV. - Develop a strategic plan for engagement including speaking opportunities, panel participations, and
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. Our solutions support researchers and research administrators across Duke University, the School of Medicine, Institutes, Centers, and Departments. We are looking for an entry-level Developer to join
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to the transformation, development, and management of enterprise information technology solutions across Duke Health. By harnessing the power of innovative technologies like cloud computing and artificial intelligence
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(FMD) primarily focusing on the (CMMS). The CMMS program is instrumental in notifying and prioritizing Operations & Maintenance preventative and corrective maintenance for the Department. The CMMS
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discovery and computational tools. The successful applicant will lead a research project and will have the opportunity to mentor students. Candidates must hold a PhD or anticipate completion of a PhD prior to
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input, with access to ongoing support and training. Qualifications : Graduation from an accredited Bachelor’s Degree in Nursing, Associate’s Degree in Nursing, or Nursing Diploma Program Twelve months
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Health Development and Alumni Affairs is seeking a visionary Senior Director, Grateful Patient Giving Program to lead strategy and innovation in patient-centered philanthropy. Be You. This role will shape
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bachelor's degree in botany, biology, zoology, psychology or other science related scientific field program. Work requires two years of research experience. A related master's degree may offset required years
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simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments