Onsite FNP Instructor, Learning Lab - Salt Lake City, UT - Part Time

About this position

Job Type: Part time Job Number: JR-026226 Location: Salt Lake City, UTIf you're passionate about building a better future for individuals, communities, and our country-and you're committed to working hard to play your part in building that future-consider WGU as the next step in your career. Driven by a mission to expand access to higher education through online, competency-based degree programs, WGU is also committed to being a great place to work for a diverse workforce of student-focused professionals. The university has pioneered a new way to learn in the 21st century, one that has received praise from academic, industry, government, and media leaders. Whatever your role, working for WGU gives you a part to play in helping students graduate, creating a better tomorrow for themselves and their families. The salary range for this position takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. At WGU, it is not typical for an individual to be hired at or near the top of the range for their position, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: Grade: Faculty 507 Pay Range: $38.79 - $58.17 Job Description **This is an ONSITE position in the WGU Clinical Learning & Simulation center in Salt Lake City, UT. Ability to work 2 shifts per month during lab operation hours (Tuesday, Wednesday, Thursday 7:30 am - 4:30 pm) required.** The Instructor, Learning Lab is a clinically experienced subject matter expert who is responsible for facilitating student learning and performance assessment(s) in on-site Clinical Learning Labs. Student learning activities in Clinical Learning Labs range from learning basic nursing skills to resolving simple-to-complex patient care situations via high-fidelity simulati

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