The Assistant Professor of Biomedical Data Science will spend 50% of their time (i) effectively teaching computer science, 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 40% of their time demonstrating evidence of or potential for research, and 10% of their time on institutional service, demonstrating the ability to work collaboratively with on-campus and off-campus colleagues and constituencies.

• 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 • Ability to lead and supervise students in academic research. • 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. • 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 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

• 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.