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development, validation, and implementation of artificial intelligence models using multimodal imaging and clinical datasets. What You'll Do: Data Acquisition & Management Organize, curate, and maintain large
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federally funded studies while developing an independent program of scholarship aligned with the HEEAT Lab mission. Working closely with community partners, health systems, policymakers, and interdisciplinary
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for postdoctoral fellows. We are seeking highly motivated candidates to join our research program in regeneration genomics. Our laboratory develops and applies cutting-edge genomics and proteomics technologies
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interested in understanding how errors arise and compound in extended or agentic workflows, and how humans can efficiently assess and verify AI-generated work. Possible research directions include developing
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collaborative modeling of marine mammal species distributions, with a focus on developing management-ready results and accompanying manuscripts suitable for immediate application to ongoing protected species
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leading experts in autism research, computer vision, machine learning, psychiatry, and developmental science to develop innovative technologies that improve how caregiver-child interactions and
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join a highly collaborative, interdisciplinary research environment focused on developing and applying cutting-edge machine learning and deep learning methods to abdominal imaging. Working alongside
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insight in collaboration with Department of Medicine and Duke Cancer Institute investigators. You will develop your research skills under faculty mentorship while contributing to collaborative cancer
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appointment. Preferred Qualifications: Experience in wet-lab research in tumor microenvironment or molecular oncology. Experience in assay development and experimental optimization. Experience in preparation
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multidisciplinary research team developing and evaluating AI-enabled approaches to identify women who may benefit from HIV pre-exposure prophylaxis (PrEP). The work brings together electronic health record data