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motivated individual with experience in deep learning and a PhD in computer science, electrical engineering, biomedical engineering, biomedical informatics, biostatistics or a related discipline. Required
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, innovation, and scientific excellence. What You'll Bring: Required Qualifications MD, PhD, or equivalent doctoral degree in Clinical Neurophysiology, Epilepsy, Neuroscience, Neurology, Biomedical Sciences
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. This position is designed for scientists with strong computational and quantitative training who are interested in agent-based modeling, network science, infectious disease dynamics, uncertainty quantification
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Appointee holds a PhD or equivalent doctorate (e.g. ScD, MD, DVM). Candidates with non-US degrees may be required to provide proof of degree equivalency. 1. A candidate may also be appointed to a postdoctoral
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-throughput in vitro screening; 2) mechanistic studies, and 3) in vivo validation PhD in pharmacology, toxicology, biochemistry, cell biology, chemical biology, or a closely related field Hands-on experience
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effectively with other members of the research team. Requirements: Earned a doctorate (PhD, MD/MPH, etc.) in implementation science Demonstrated proficiency through previous research experience implementation
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, Physics, Applied Mathematics, Computer Science, or a related area. Experience 0+ years of postgraduate experience. Skills The candidate must have excellent skills in conducting independent research and
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international programs in more than 150 countries. The Pratt School of Engineering at Duke University is one of the fastest rising, highly ranked engineering schools in the nation. The school consists of four
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postdoctoral Research Associate (computational biology and bioinformatics area) position is open at Duke University School of Medicine in the lab of Dr. Yi Zhang starting Mar 2026 or later. The ideal candidate
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communities in the gut, blood and other bodysites; immune profiling; biomarker assays; metabolomics; clinical outcomes research; and systems-based biology and computational multi-omics approaches