28 machine-learning "https:" "https:" "https:" Postdoctoral positions at Stanford University
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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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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with neuroimaging and neural signal processing tools, including fMRI, structural MRI, diffusion MRI, EEG, or related modalities. Strong publication record in AI, machine learning, computational
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renewable Appointment Start Date: As soon as possible, but later starting date also considered Group or Departmental Website: https://www.mignotlab.com(link is external) How to Submit Application Materials
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paired with computational biology and machine learning to develop predictive AI models of how cells interpret and respond to the surrounding extracellular matrix. Required Qualifications: We are looking
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engineering and clinical physiology. Projects may involve signal quality assessment, artifact detection, waveform segmentation, feature extraction, hemodynamic modeling, time-series analysis, machine learning
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Nutrition Postdoc Appointment Term: Fixed term for one (1) year with opportunity for renewal Appointment Start Date: ASAP Group or Departmental Website: https://colmanlab.stanford.edu/(link is external) How
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field Demonstrated expertise in one or more of the following areas: Machine/deep learning, artificial intelligence, statistical modeling, or computational modeling Human neuroimaging analysis, including
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2026 Group or Departmental Website: https://mellab.stanford.edu/(link is external) https://spoglab.stanford.edu/(link is external) How to Submit Application Materials: To apply, please submit
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, inferential and spatial analysis, as well as integrating machine learning techniques, and leading and drafting manuscripts. We will support and encourage the candidate’s independent research interests