226 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at University of Alabama at Birmingham
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physician-scientists, cares for 20,000 patients annually, and is home to more than 160 employees, plus all the physicians and clinical staff engaged in cancer patient care. The link to our website is: https
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leadership development and community engagement initiatives aligned with departmental learning outcomes and divisional priorities. Manages signature and ongoing leadership and service experiences, ensuring
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. Willingness to learn clinical skills such as phlebotomy, obtaining vital signs, and Pulmonary Function Testing is desirable. Candidate must be comfortable using a variety of electronic platforms. Possess
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Enterprise (CReS)t operations, decision-making, and productivity. It includes building tools that leverage machine learning and generative AI, as well as applications for querying, reporting, and operational
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. • Must have used an electronic health record system in a previous learning or employed role. Knowledge, Skills & Abilities: • Ability to perform patient care as directed by a physician, nurse
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improvement of processes. Applies knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business
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RN CLINICAL EDUCATOR-SON (2) - (T233378) Description The Nursing Clinical Educator role supports undergraduate/prelicensure student learning experiences focused on mastery of competencies
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. To instruct student athletes in proper techniques and decision-making to obtain desired outcomes. To adhere to all institutional, conference and NCAA rules and regulations. May assist with seasonal student
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effectively conveys information verbally and in writing. Have stamina, drive and perseverance to handle pressure well. Must be willing and able to resolve problem situations. Must be a computer-literate
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, implement, and optimize pulmonary CT and MRI image processing algorithms Develop AI and machine learning models tailored for lung imaging applications, including segmentation, classification, and disease