72 machine-learning-postdoc Postdoctoral positions at Duke University in Ireland-University-Ranking-2024
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to iteratively refine models and optimize experimental design. Collaborate with another postdoc in the NIH Center to use scientific machine learning (SciML) to automatically select mathematical models from data
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, learning, well-being, social experiences, and decision-making. HARP is developing a longitudinal multimodal research resource to study human-AI interaction and its impacts using student-informed, privacy
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experimental systems, from cell lines to primary neurons to ex vivo brain tissue. The postdoc will be encouraged to develop a unique scientific identity within the broader project, lead publications, present
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rapidly changing priorities and have ability to quickly learn new skills. Must be detail-oriented, well organized with strong communication skills and ability to work in an interactive team environment
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Be You. At Duke, we celebrate individuality and the unique perspectives that each member of our community brings. As the Machine Learning Research Data Analyst, Ophthalmic Imaging, you'll play a
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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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Model and Data Sharing portal. Work collaboratively with graduate students, postdocs, software engineers, faculty, and senior researchers from Duke University and UNC Chapel Hill. Participate in research
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likely. The postdoc will provide resources, and assist in the development of deliverables for the projects. This position will be expected to participate in project leadership and management, writing
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material damage assessment 3) Developing AI and machine learning models for robot-assisted laser surgery and validate the model by comparing the results to experimental observations. 4) Support the
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disorders, with a particular emphasis on glycogen storage diseases (GSDs), including GSD type II (Pompe disease). The postdoc will contribute to advancing understanding of the natural history of GSDs and