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Field
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on qualifications and experience), with a speciality in statistical image processing to work with Professor Karen Desmond’s research team on the project ‘Polyphonic Singing and Communities of Music Writing in
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applications for a 12-month position as either a Research Assistant (Level 2) or Postdoctoral Researcher (Level 1) (depending on qualifications and experience), with a speciality in statistical image processing
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‘, bridges the different techniques developed within the cross-area imaging and gives support to all researchers dealing with image processing challenges. By focusing on automation and enabling correlative
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ABIF can be found at: http://www.mcgill.ca/abif. Primary Responsibilities 1. Technical Development & Innovation Lead and document quality control (QC) workflows for ABIF imaging systems, including
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You will: Lead a project on synaptic and circuit mechanisms underlying sensory processing in V1 Implement advanced in vivo approaches, including two-photon imaging, optogenetics, and/or
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in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses on methodological development in cryo
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago
cycles. Understanding these complex interactions requires measurements that go beyond traditional multi-spectral imaging. Imaging spectroscopy—capturing the full contiguous spectrum from the Visible
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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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following skills: Strong interest in the field of neuroimaging, psychiatry and genetics. Computer skills: Strong level in the main informatics software (FSL, Freesurfer, fMRIprep) and coding languages (R
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multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities