615 computer-programmer-"https:"-"UCL" "https:" "https:" "https:" "https:" "https:" "https:" uni jobs at University of Virginia
Sort by
Refine Your Search
-
Listed
-
Field
-
supervision and collaborating with and mentoring graduate and undergraduate students, QUALIFICATION REQUIREMENTS: A Ph.D. in Psychology, Human Development, Political Science, Computer Science, Data Science or
-
will be given to candidates who have received their Ph.D. within the last three years. Application Process: Apply online at https://uva.wd1.myworkdayjobs.com/UVAJobs and also submit an application
-
throughout the program as determined by their competency and overall clinical development. Fellows are evaluated on their clinical performance and competencies after each rotation. Full-time Clinical Schedule
-
Tuition and professional development benefits Employee wellness program featuring activities to earn up to $500/year Selected candidates will be required to complete all required background checks and
-
. Candidates must have a strong commitment to teaching and mentorship. Applicants must be eligible for RN licensure in Virginia where applicable. To teach in the APRN program, applicants must be eligible
-
eligible for leave or other benefits and is limited to 1500 hours of work in a year. For more information, refer to the Wage Employment link: http://uvapolicy.virginia.edu/policy/HRM-029 To apply, please
-
professional development benefits Employee wellness program featuring activities to earn up to $500/year Physical Demands: The employee will be required to bend, stoop, squat and walk while making frequent
-
generated by nursing research. In addition to the above job responsibilities, other duties may be assigned. MINIMUM REQUIREMENTS Education: Graduate of an accredited nursing program required. Bachelor
-
needs Retirement through the Virginia Retirement System Tuition and professional development benefits Employee wellness program featuring activities to earn up to $500/year The selected applicant will
-
multidisciplinary experience in combining integrative computational immunology – data-driven, state-of-the-art single cell resolution and spatial methods, machine learning and kinetic modeling – with integrative