318 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" positions at Stanford University in United States
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individuals who are passionate about science, learning, and collaboration. Candidates with a strong background in molecular and cellular biology, and/or experience in bioinformatics, will find this an ideal
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Appointment Start Date: Immediately Group or Departmental Website: https://med.stanford.edu/lulab.html (link is external) How to Submit Application Materials: Email the required application materials to bingwei
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complete the Stanford application process and submit their CV and letter of interest thru the Stanford Careers website - https://careersearch.stanford.edu/ referencing job number: 108273. The expected pay
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skills. * Proficient computer skills and experience with office software and email applications such as Microsoft Office, Adobe Acrobat, etc. * Proficiency in content management systems (e.g. AEM, Website
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Stanford University, recognized as one of the most prestigious and innovative academic institutions in the world, has an opening for an Office Manager & Executive Assistant in the Department of Learning
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and/or cutting edges machine learning techniques to make foundational discoveries in reproductive medicine. The annual salary for this full-time position starts at $76,383, dependent upon skills and
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preferred. PHYSICAL REQUIREMENTS*: Frequently stand, walk, twist, bend, stoop, squat and use fine light/fine grasping. Occasionally sit, reach above shoulders, perform desk based computer tasks, use a
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Appointment Term: 2 years Appointment Start Date: July 1, 2026 Group or Departmental Website: https://ed.stanford.edu/faculty/ksadow (link is external) How to Submit Application Materials: Fill out
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connectivity and graph-theoretic analyses Familiarity with MR sequence programming (Siemens or GE platforms) Machine learning / AI applied to neuroimaging data EEG acquisition and analysis Use of neuroanatomical
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and patient-reported outcomes; (b) observational research and comparative effectiveness studies; (c) intervention studies; (d) clinical informatics, mobile/electronic health; (e) machine learning