100 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" research jobs at Stanford University in United States
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to apply theoretical knowledge of science principles to problem solve work. Ability to maintain detailed records of experiments and outcomes. General computer skills and ability to quickly learn and master
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one or more of the following areas is a BIG PLUS: data science (machine learning and AI), cancer biology, animal physiology, organic chemistry, E3-ubiquitin biology, and gene editing. In all cases
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: 2 years Appointment Start Date: 9/1/2026 Group or Departmental Website: http://buddhiststudies.stanford.edu (link is external) How to Submit Application Materials: Applications should be submitted via
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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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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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: 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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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/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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and patient-reported outcomes; (b) observational research and comparative effectiveness studies; (c) intervention studies; (d) clinical informatics, mobile/electronic health; (e) machine learning