Assoc Professor

MinimumQualifications:

• Doctoral degree in Public Health or closely related field or the equivalent.
• Two years of continuous and productive accomplishment as an Assistant Professor at an accredited University in Public Health or closely related field.
• Record of excellence in teaching courses in epidemiology, including a clearly defined teaching philosophy, participation on thesis and/or dissertation committees, and successful direction of the work of master’s and doctoral candidates.
• Record of excellence in research as exemplified by a continued record of collaborative epidemiological research and scholarship (e.g., peer-reviewed publications, local/state/national presentations) and current extramural funding.
• Record of substantial contribution of service to a university and to the field, profession, and community as appropriate to the mission and goals of the college and/or university.
• Record of collegiality and participation as a citizen of a university.
  

Preferred Qualifications:

• Demonstrated commitment to diversity, inclusion and social justice in teaching, research and service.
• Record of obtaining extramural research funding through competitive processes, with past or current principal investigator status on national level research grants. Though not required, funding in the areas of cancer, environmental/occupational, or infectious disease epidemiology is preferred.
• Experience in practical applications of epidemiology to the Public Health practice field.
• Evidence of generating and translating research to practice, policy, and programs.
• Experience in teaching graduate courses.
• Evidence of innovation in teaching in the classroom and on-line, curriculum development, mentoring and supervising graduate students.
• Demonstrated ability to work collaboratively with multidisciplinary teams.
• Demonstrated record of leadership in community or professional organizations.
• Experience using state-of-the-art epidemiological methods in data analytics including those referred to as “big data”, “omics”, “data mining”, and/or “machine learning”.