53 machine-learning "https:" "https:" "https:" "https:" "https:" positions at Broward College
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preferred. • The successful candidate must have a high level of computer literacy and a commitment to teaching. • The successful candidate must be willing and able to teach in both the traditional face-to
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. Minimum Experience/Training: Prior college teaching experience is preferred. The successful candidate must have a high level of computer literacy and a commitment to teaching. The successful candidate must
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candidate must have a high level of computer literacy and a commitment to teaching. The successful candidate must be willing and able to teach in both the traditional face-to-face and blended (hybrid) format
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. The successful candidate must have a high level of computer literacy and a commitment to teaching. The successful candidate must be willing and able to teach in Fully Online format. All schedules require office
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evaluation, orientation, assessment of learning, facilitating student engagement in academic and career planning activities, student success skills, college policies and procedures, and student co- and extra
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graduate credit hours in Mass Communication, Strategic Communication, or New Media Communication. Minimum Experience/Training: Experience in evidence-based teaching, active learning, problem based learning
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evaluation, orientation, assessment of learning, facilitating student engagement in academic and career planning activities, student success skills, college policies and procedures, and student co- and extra
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candidate must have a high level of computer literacy and a commitment to teaching. The successful candidate must be willing and able to teach in both the traditional face-to-face and blended (hybrid) format
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Nuclear Medicine Technology AND Certification and registration in Nuclear Medicine Technology (NMTCB or ARRT) AND Experience to teach assigned courses Minimum Experience: Prior college teaching experience
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underprepared students achieve academic success. The successful candidate will be expected to learn to use and maintain the use of computer assisted referral software in identifying students at academic risk and