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                multiple affiliations. Postdoctoral FellowPosition Computational approaches to malaria parasite antigen diversity Duke Global Health Institute Be You. The Malaria Collaboratory is recruiting an exceptional 
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                programs in the liberal arts, medicine, business and law, Pratt prepares future engineering leaders to address complex challenges with solutions that take into account the complexities and nuances 
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                , evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control 
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                healthcare. Qualifications Required: PhD (or equivalent) in computer science, statistics, biostatistics, electrical/biomedical engineering, or related quantitative field. Strong background in machine learning 
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                Duke University, Electrical and Computer Engineering Position ID: Duke -Electrical and Computer Engineering -POSTDOCYIRANCHEN [#30336] Position Title: Position Type: Postdoctoral Position Location 
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                , planning, coordinating, and executing scholarly events; and a deep interest in helping an existing program grow. There is considerable space for a visionary postdoctoral fellow to bring their interests and 
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                . Occupational Summary The David Lab at Duke University (www.ladlab.org ) is recruiting a postdoctoral fellow to join an established research group developing and applying DNA sequencing and computational 
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                , genomics, microbial physiology, and metabolomics approaches. The candidate is expected to work collaboratively with other members of our interdisciplinary lab, including computational and other experimental 
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                experience in optics, computation and electronics. Responsibilities will include designing and implementing optical systems based on quantitative phase imaging with digital holography and electronic systems 
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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