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Post-Doctoral Position - Medical Image Processing - (240000Y9) Post-Doctoral Fellow Position in Medical Image Processing (Deep Learning for Trauma CT) The Trauma Radiology AI Lab (TRAIL) in
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Position Overview School / Campus / College: School of Medicine Organization: Radiology Title: Postdoctoral Scholar – Department of Radiology, Cancer and Tumor Imaging, OncoRad Lab Position Details
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, or Biophysics, with strong quantitative and analytical skills and proficiency in MATLAB and/or Python. Experience with preclinical MRI/fMRI/MRS, rodent models, image processing, functional connectivity, and
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cleaning, standardization, and quality assurance activities to support reproducible research. Image Processing & Annotation Preprocess ophthalmic images for machine learning applications through resizing
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CT imaging CT reconstruction and image-processing methods Material decomposition and quantitative spectral imaging Image segmentation, registration, and quantitative morphometry Machine learning and
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, Machine Learning, Data Science, Computer Science, Electrical Engineering, Biomedical Engineering, or a related field. Experience with image analysis, computer programming using Python and R, and machine
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imaging modality. Preferred Qualifications: Work Experience: Preclinical MRI MRI pulse sequence development and optimization Quantitative MRI analysis Image reconstruction and image processing using Bruker
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the internal application process. Welcome to The Ohio State University's career site. We invite you to apply to positions of interest. In order to ensure your application is complete, you must complete
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are in place. Required Qualifications: Doctoral degree conferred prior to appointment start date. The ideal candidate would have experience in at least one of the following: mouse brain surgery, activity
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 14 days ago
Hill seeks a computational postdoctoral researcher to develop and apply methods for integrating single-cell sequencing and imaging data within controlled cell-line model systems. The project focuses