317 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Stanford University in United States
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relevant experience. To be successful in this position, you will bring: Slate, PeopleSoft experience highly desirable. Knowledge of computer system capabilities, business processes, and work flow. Experience
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above shoulders, perform desk based computer tasks, use a telephone and write by hand, lift, carry, push, and pull objects that weigh up to 40 pounds. * Rarely kneel, crawl, climb ladders, grasp
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: Jewish Studies Postdoc Appointment Term: one year Appointment Start Date: September 1, 2026 Group or Departmental Website: https://jewishstudies.stanford.edu/ (link is external) How to Submit Application
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or Departmental Website: https://med.stanford.edu/michellelinlab.html (link is external) How to Submit Application Materials: Please send materials to PI Dr. Michelle Lin (mplin [at] stanford.edu) with the subject
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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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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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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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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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Biology Stanford Cancer Center Postdoc Appointment Term: Open-ended. Appointment Start Date: ASAP Group or Departmental Website: https://rogala.stanford.edu/ (link is external) How to Submit Application
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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