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primary benefit. This postdoctoral appointment is part of the Duke University Aging Center’s NIA-funded T32 Postdoctoral Research Training Program. This multidisciplinary program is focused on developing
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, evidence-based practice, and interdisciplinary collaboration to lead the future of nursing. Our nursing program stands as a leader in healthcare education, earning #1 in the nation for Best Bachelor
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international and regional leadership in biostatistics, genomics, biomedical informatics, artificial intelligence and health data science. We are recruiting a creative, rigorous Postdoctoral Research Scientist
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systems, social sciences, epidemiology or other health-related fields. Demonstrated proficiency through previous developed analytical codes with R Language for Statistical Computing and/or Python The ideal
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) Experimental investigation and computational model simulation of laser-induced bubble dynamics and material damage assessment 3) Developing AI and machine learning models for robot-assisted laser surgery and
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the university and with industry to translate discoveries into clinical proof of concept studies. Working with a team led by Drs. H. Kim Lyerly, Zachary Hartman and Josh Snyder, this program spans basic discovery
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Education Doctoral degree (Ph.D. or Psy.D.) in clinical or counseling psychology from an APA approved program. Minimum Experience Successful completion of an APA-accredited doctoral internship program
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. The appointment is viewed as preparatory for a full time academic or research career. The appointment is not part of a clinical research training program, unless research training under the supervision of a senior
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or scholarship, and may include teaching responsibilities. The appointment is generally preparatory for a full time academic or research career. The appointment is not part of a clinical training program, unless
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to therapies and vaccines against human diseases. We are a team of highly interactive investigators that have expertise in immunology, molecular biology, virology, microbiology, structural biology, computational