88 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions at Stanford University
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of Dr. Taia Wang at Stanford University is recruiting a postdoctoral fellow to study human antibody biology. We are an experimental, mechanism-driven discovery lab focused on identifying the organizing
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. The ideal candidate will possess not only a deep conceptual understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. We
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entanglement in the photoionization of small molecules driven by extremely intense laser fields. The successful candidate will be based at Stanford University in the group of Prof. Philip H. Bucksbaum, working
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(HAI)(link is external) . Research Focus The fellow will lead computational modeling efforts to develop large-scale, multimodal models of the human brain. The work will involve integrating brain imaging
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experience may include flames, plasmas, high-temperature CVD furnaces, aerosol reactors, or related gas-phase synthesis platforms. The position combines reactor development, chemical kinetics, reacting-flow
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sequencing datasets followed by functional validation in murine models. Our long-term goals are to utilize this information to develop cellular therapies for pediatric AML. Required Qualifications
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position is supported by a National Institute on Drug Abuse-funded R01 award. The position involves contributing to a dynamic team engaged in developing and applying a combination of statistical analyses and
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Pancreas Program seeks an exceptional Postdoctoral Scholar to lead hypothesis driven mechanistic research focused on how vitamin A signaling and transport regulate inflammation, tissue injury, and recovery
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, observations, a hierarchy of numerical models, and machine-learning methods to understand their formation, dynamics, and predictability. The successful candidate will have substantial freedom to develop
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continuous monitoring data. Development of machine learning models to identify physiologic instability or changes in patient status. Validation of waveform-derived metrics against clinical reference standards