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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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related to staff position within a Research Infrastructure? No Offer Description Postdoctoral Researcher – Automated Correlative Image Analysis for BlueMaterials Reference code: 958 - 2026/MB 1 Work
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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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, 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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focused on deep-phenotyping of individuals with autism and controls including brain imaging (MRI, fMRI, DTI and EEG) and a battery of cognitive tests. Our group is currently developing new methods
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 32 minutes ago
to geysers on Triton. These images have been used countless times over the past three decades to conduct scientific analyses in planetary geology and atmospheric science, to inspire new generations
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of myocardial fibrosis using 68Ga-FAPI-04 PET/CT (Protein Positron Emission Tomography associated with Computed Tomography using Gallium-68-labeled Fibroblast Activation Inhibitor), integrating multimodal imaging
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and the endoplasmic reticulum. The project will combine approaches in cell biology, biochemistry, and advanced imaging. The candidate will contribute to the development and optimization of experimental
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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