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Job Description Required Qualification: (as evidenced by an attached resume) * Doctoral degree (or foreign equivalent) in optical imaging, biomedical engineering, microbiology, multi-omics, or a closely
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including hanging protocols. Acts as preceptor for imaging team members. Acts as super user for imaging technology implementations and training initiatives. Supports residents, fellows, and faculty members
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as preceptor for imaging team members. Acts as super user for imaging technology implementations and training initiatives. Supports residents, fellows, and faculty members with imaging presentations
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, biosystems engineering, or closely related fields Strong command of written and communication skills Experience in image processing, working with plants, and using AI is desirable Proven ability to collaborate
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qualification; (ii) demonstrate research skills in imaging science and machine learning, particularly on image reverse engineering, fake image and video detection, statistical detection models and mathematical
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analysis Experience working with R and Python for data analysis and image processing Familiarity with image analysis tools (e.g., ImageJ/Fiji, CellProfiler, or similar platforms) is desirable Education and
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University of California San Francisco | San Francisco, California | United States | about 2 months ago
-processing systems. Collaborates with UCSF Clinical Systems in developing patient-centric imaging solutions and procedures. Manages Service Delivery to all Imaging IT customers. Supports small project
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of imaging technology and that drives the dissemination of that technology through a multi-pronged education strategy to scientists around the world. ABC was intentionally designed to maximize scientific
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of a 3D ultrasomics AI prognostic system for adolescent idiopathic scoliosis progression via longitudinal surveillance scans”. Qualifications Applicants should have experience in image processing and
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protocols using aerial and ground-based platforms for agricultural safety monitoring. Apply advanced image analysis and computer vision techniques to identify and classify food safety risks in agricultural