86 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Stony Brook University
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converter degradation/failure analysis. Brief Description of Duties: The Postdoctoral Associate will support the Principal Investigator, Dr. Fang Luo , in the Department of Electrical and Computer Engineering
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Computer Engineering or a closely related field, completed by the start date of the appointment. Research experience in at least one of the following areas: ● Chip design, tape-out, and testing
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Job Description Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in Biomedical Engineering, Medical Physics, Electrical, Computer Engineering or a closely
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in Biomedical Engineering, Medical Physics, Electrical, Computer Engineering or a
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Computer Engineering or a closely related field, completed by the start date of the appointment. Research experience in at least one of the following areas: ● Chip design, tape-out, and testing
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in Biomedical Engineering, Medical Physics, Electrical, Computer Engineering or a
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to neurovirulence. Preferred Qualifications: PhD or Postdoc experience in Flavivirus research. Experience in BSL3 with neuropathogenic viruses and ABSL3 murine models. Two (2)- Five (5) years of molecular biology
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to neurovirulence. Preferred Qualifications: PhD or Postdoc experience in Flavivirus research. Experience in BSL3 with neuropathogenic viruses and ABSL3 murine models. Two (2)- Five (5) years of molecular biology
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experiments in the field of neuroscience, computational neuroscience and biologically plausible machine learning tools. Duties: Analyze data and developing biologically plausible machine learning frameworks
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experiments in the field of neuroscience, computational neuroscience and biologically plausible machine learning tools. Duties: Analyze data and developing biologically plausible machine learning frameworks