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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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for highly motivated postdoctoral candidates with a PhD in bioengineering deep knowledge in computational biology and machine learning. Candidates with a molecular biology or engineering degrees with
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monitoring systems, bedside monitoring devices, or medical device data. Experience linking physiologic waveform features to clinical outcomes. Experience with machine learning, deep learning, predictive
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Stanford Departments and Centers: Environmental Social Sciences Postdoc Appointment Term: 12 months renewable for an additional 2 years based on satisfactory performance and availability of funding
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, deep learning, fNIRS, eyetracking). The postdoc will primarily be mentored by Meg Cychosz (Linguistics), but will have ample opportunity and flexibility to establish collaborations with other groups and
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Pediatrics Postdoc Appointment Term: 2 year Appointment Start Date: Open position How to Submit Application Materials: https://forms.gle/xuUxLuwf4cTcBvWs9(link is external) Does this position pay above the
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) conferred by start date Demonstrated experience with imaging and/or video datasets Training and experience in machine learning, computer vision, and deep learning methods Excellent English language
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: The candidate must have a PhD and extensive experience in modern deep neural network-based techniques. The ideal candidate should have: A PhD and a strong record of research or applied work in deep learning, with
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appointed through the Office of Postdoctoral Affairs. The FY27 minimum is $79, 056. Precision mapping of vector borne diseases using deep learning & high resolution remote sensing data Faculty in Stanford
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imaging, genetics, omics, EHR data, and clinical outcomes. Ongoing work builds on deep-learning phenotypes from cardiovascular imaging at population scale and extends toward myocardial tissue remodeling