85 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Stanford University
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independent research, the postdoctoral fellow is expected to teach one (quarter-long) course, generally at the undergraduate level, per year, and contribute more broadly to the life and activities of the Center
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. The position is fully funded. Required Qualifications: The successful candidate should be highly motivated and hard working, with outstanding past research success and publication history, with an MD, PhD or MD
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research expenses. Please direct queries to Professor Lisa Surwillo (surwillo@stanford.edu (link sends e-mail) ). Required Qualifications: Applicants must have received a PhD from an accredited university in
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, calcium imaging and animal handling experience are encouraged to apply. Required Qualifications: Completion of PhD training. Previous experience in vision or neuroscience research is ideal. Required
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learning experts will be an essential and enriching component of the position. Strong candidates will have a background in machine learning and natural language processing (NLP), with a demonstrated ability
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Qualifications: Applicants must have received a PhD from an accredited university in German Language and Literature, History, Religious Studies, Philosophy, or a related field before the appointment start date A
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at the Stanford University School of Medicine is seeking candidates for a Postdoctoral Fellow position to perform MRI research of the visual system for guiding vision preservation and restoration. This person is
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Posted on Thu, 03/20/2025 - 22:13 Important Info Deprecated / Faculty Sponsor (Last, First Name): Dirbas, Frederick MD Other Mentor(s) if Applicable: Billy Loo, MD, PhD, Ted Graves, PhD Stanford
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Required Qualifications: PhD in Neuroscience, Bioengineering, Electric Engineering, Computer Science, Physics, or a related field Strong quantitative and analytical skills Experience in either experimental
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, EBV, HBV, HPV) and chemical biology trainees with a willingness and aptitude for learning new experimental systems. Our projects require an adaptable mix of molecular virology as well as protein