317 machine-learning "https:" "https:" "https:" "https:" "https:" "Cardiff University" positions at Stanford University in United States
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or Departmental Website: https://med.stanford.edu/gruberlab.html (link is external) How to Submit Application Materials: Interested applicants should submit the required application materials by email to Aronne
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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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: Postdoc positions renew yearly with an ideal total duration of 3 to 5 years. Appointment Start Date: Flexible (January-September 2026) Group or Departmental Website: http://hernandez-nunez-lab.com (link is
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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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constraints Ability to coach, give and accept performance feedback; train and develop staff PHYSICAL REQUIREMENTS: Frequently stand/walk, sit, perform desk-based computer tasks and lift/carry/push/pull objects
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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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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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. Appointment Start Date: Preferably by July 2026 Group or Departmental Website: https://hph.stanford.edu/ (link is external) How to Submit Application Materials: Submit all application materials
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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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and patient-reported outcomes; (b) observational research and comparative effectiveness studies; (c) intervention studies; (d) clinical informatics, mobile/electronic health; (e) machine learning