2,113 computer-"https:"-"APOS-UFFICIO-CONCORSI-DOCENTI" "https:" "https:" "https:" "https:" "J. F" positions at Duke University
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. or equivalent doctorate (e.g. Sc.D., M.D., D.V.M.) in medical physics, biomedical engineering, electrical engineering, or computer science. Candidates with non-US degrees may be required to provide proof
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comprehensive and competitive medical and dental care programs, generous retirement benefits, and a wide array of family- friendly and cultural programs to eligible team members. Learn more at: https
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discover how we can advance health together. Duke Health Integrated Practice https://careers.dukehealth.org/us/en/dhip Duke Health Integrated Practice comprises more than 110 primary and specialty outpatient
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independently and collaboratively. High proficiency with computer-based technologies and adaptability to new tools. Excellent organizational and prioritization skills. Commitment to outstanding customer service
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discover how we can advance health together. Duke Health Integrated Practice https://careers.dukehealth.org/us/en/dhip Duke Health Integrated Practice comprises more than 110 primary and specialty outpatient
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career. The appointment is not part of a clinical training program, unless research training under the supervision of a senior mentor is the primary purpose of the appointment. The Postdoctoral Appointee
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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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cultural programs to eligible team members. Learn more at: https://hr.duke.edu/benefits/ Minimum Qualifications Education: Work requires knowledge of construction and renovation project finances and
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programs to eligible team members. Learn more at: https://hr.duke.edu/benefits/ DEPARTMENTAL PREFERENCES Hospital Facilities experience preferred. Must have knowledge of The Joint Commission, CMS, DFS, OSHA
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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