The Assistant Professor of Biomedical Data Science will spend 62.5% of their time (i) effectively teaching computer science, bioinformatics, data science, and biomedical data science courses, (ii) advising and mentoring School of Applied Computational Sciences (SACS) students, and (iii) developing instructional methods that will reflect best practices in pedagogy. The successful candidate will also spend 27.5% of their time demonstrating evidence of or potential for health disparities research utilizing data science techniques, and 10% of their time on institutional service, demonstrating the ability to work collaboratively with on-campus and off-campus colleagues and constituencies.
Daily Operations
Demonstrated ability to communicate effectively verbally and in writing.
Ability to integrate technology effectively and appropriately into the teaching and learning process.
Ability to successfully interact with students, other educators and educational institution representatives, and the public in a professional manner.
Ability to plan, evaluate and revise curricula, course content and course materials and methods for courses within the SACS
Maintain regularly scheduled office hours to advise and assist students
Collaborate with colleagues to address teaching and research issues
Assist in working on graduate internships in health disparities
Ability to lead and supervise students in academic research related to problems in health disparities utilizing artificial intelligence and machine learning techniques.
Keep abreast of development in your field by reading current literature, talking with colleagues, and participating in professional conferences
Develop and deploy impactful and socially-responsible scientific knowledge and practical technologies that empower society to improve the quality of life
Ability to work collaboratively across university departments.
Participate and lead grant writing/submission, scholarly publications in refereed journals, and research design.
Assist in creating and eventually lead a seminar series focused on the use of biomedical data science to eliminate health disparities.
Serve on academic or administrative committees that deal with institutional policies, departmental matters, and academic issues
Participate in campus and community events
Required Skills
Use of mainstream programming languages for biomedical data science, including Python, R, SQL, and SAS.
Proficient in multivariate statistical mathematics in biomedical applications
Use of "other" potential programming languages including Java, Scala, Julia, TensorFlow, Go, Spark
Excellent written and verbal communication skills.
Excellent interpersonal communication skills.
Sound judgment and maturity, exemplified by consistent professionalism in working with individuals at all levels, both internally and externally.
Required Education and Experience
Earned doctorate degree in computer science, data science, bioinformatics, biophysics, or similar with emphasis in artificial intelligence and or machine learning
Experience in biomedical applications of reinforcement learning, Markov decision processes, machine learning techniques, AI, et al.
At least 3 years of successful teaching experience is preferred, but applicants with the desire to develop expertise in teaching and mentoring are also invited to apply.
Publication track record commensurate with career path history time-line
Ability to develop, lead, and obtain external funding in research within 4 years.
