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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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will play a key role in automated wildlife identification and classification from trap camera images using cutting-edge computer vision technology. Working closely with the Principal Investigator, Co-PI
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Practice, Associate Professor of Clinical Practice, or full Professor of Clinical Practice to join the Division of Cardiology as a member of the Non-Invasive Imaging Team. General expectations: Perform
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, this segmentation may rely on artificial intelligence (AI) tools trained to automatically identify and delineate the main organs from MR images. This step will make the generation of attenuation maps faster, more
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environment Help maintain study documentation and manage databases (Excel, REDCap, OnCore) Assist with segmentation and analysis of MRI images Support participant recruitment (e.g., identifying and contacting
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, MATLAB, and R, combined with experience in signal and image analysis, as well as familiarity with electronic health data, will be distinct advantages. https://www.bcm.edu/departments/surgery/research/atlas
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document preparation. 10. Assists with the Office of the Provost and the Division of Academic Affairs events. 11. Contributes to special projects as needed. 12. Presents a positive, supportive image of
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variational autoencoders (VQ-VAE) or similar models Knowledge of MRI physics and Bloch equation simulations Experience with medical image segmentation or classification tasks Publications in machine learning
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for the division, under the direction of the Imaging Manager. Minimum Qualifications 2-Year College Degree. A.R.R.T. Radiography registered with current ODH licensure. Previous interventional radiology experience is
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Radiologist to join our expanding team in the Breast Imaging Division. This is an exciting opportunity for a dynamic physician to participate in a growing clinical program within an academic health system