Visiting Assistant Professor of Informatics and Machine Learning

The Public Health Informatics Technology (PHIT) Program in the College of Health Sciences is accepting applications for its Visiting Assistant Professor of Informatics and Machine Learning position. This non-tenure position includes teaching graduate-level courses in Data Analytics and Health Informatics. Faculty will coordinate students’ capstone opportunities and will be expected to make substantial contributions to teaching, research/scholarly activities, and service.

• Provide results-driven outcomes as a facilitator of graduate courses.
• Integrate hands-on learning opportunities through practices and discovery-based learning strategies that address real-world problems.
• Implement 21st-century technology-enhanced learning strategies that improve students’ opportunities, academic achievement, and job preparation.
• Collaborate with external stakeholders to develop partnerships to secure funding opportunities.
• Promote, develop, and teach courses for Informatics, Data Science, and/or Machine Learning.
• Using various Learning Management Systems and Platforms (Canvas, LoudCloud, Angel, D2L, Blackboard and Module).
• Participation in program events such as: Orientation, Graduation, Students’ celebrations, Faculty/Staff Retreats, and other general events.
• Work cooperatively and collegially within a diverse environment.
• Conduct training workshop and support PHIT goals and objective.
• Perform other duties as assigned.

• Must have a Ph.D. in Computer Science or related areas.
• Experience in curriculum delivery and development.
• Experience teaching courses at graduate levels preferred.
• A strong preference will be given to those candidates with expertise in machine learning, informatics artificial intelligence, and data science who have a demonstrated and ongoing record of impactful research in Machine Learning and proven ability to link theory and practice in their area(s) of expertise.
• Demonstration of scholarly contribution through refereed journal publications and other intellectual contributions in machine learning.
• Demonstrated ability to conduct independent and interdisciplinary funded applied research.
• Strong commitment to strategic thinking.
• Excellent verbal and written communication skills.