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Field
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appropriate for individual disease processes. Use independent judgement to acquire the optimum diagnostic information for each examination performed. Perform carotid, peripheral, venous, peripheral arterial
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experience to apply those skills toward imaging data (e.g., live cell microscopy). The successful candidate will contribute to advancing machine learning-driven analysis of high-content imaging data to achieve
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and evaluate scientific papers; ability to write up scientific results and contribute to editing manuscripts; strong computer and organizational skills. About the Department The Masonic Cancer Center
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programming languages such as Matlab, Python, C/C++. Familiarity with cloud computers. Experience with MRI and medical physics. Desired Qualifications* Familiarity with Docker and Kubernetes, and their
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health records (EHR), waveforms from bedside monitors, radiology images and wearable sensors. This position offers a unique opportunity to work closely with clinicians on applications of machine learning
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images for volume navigation into ultrasound machines for procedural scanning. Post-Procedure Clean-Up: Ensure proper post-procedure clean-up. Complete exam and bill for all billable supplies & procedures
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.Performs Diagnostic Images a. Corroborates the patient's clinical history with procedure, assuring information is documented and the exam ordered following patient identification guidelines. b. Utilizes
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Physician Assistant Training in radiation safety and radiation biology Computer knowledge in MS Word, Excel, Access and other medical-based programs Familiarity with Epic based EMR Radiology experience
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technical knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques
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computer skills including computer documentation and order entry. Demonstrated experience with and knowledge of current medical, surgical, and pharmaceutical treatment modalities of the post-cardiothoracic