NUNM’s School of Undergraduate and Graduate Studies is seeking an adjunct academic faculty member to teach Biostatistics for Pre-Health Majors, Biostatistics I, and Biostatistics II.

Adjunct faculty members perform instructional duties and responsibilities in accordance with the philosophy, mission, policies, and procedures of the University and appropriate to the individual assignment. The relationship of the faculty member to the student is one of teacher, mentor and facilitator of learning.

 

Course Name: NS411: Biostatistics for Pre-Health Majors (undergraduate course)
Term: Fall 2026 (Begins September 14^th, 2026 – Ends December 12^th, 2026)
Course Credits: 3 (compensation range: $1560 – $1620)
COURSE DESCRIPTION: Topics include the collection, classification and presentation of descriptive data; the rationale of estimation and hypothesis testing; analysis of variance; analysis of contingency tables; correlation and regression analysis; multiple regression, logistic regression, and the statistical control of confounding; sample size and power considerations; and survival analysis.

Course Name: RES600: Biostatistics I (graduate course)
Term: Winter 2027 (Begins January 4^th, 2027 – Ends March 27^th, 2027)
Course Credits: 2 (compensation range: $1560 – $1620)
COURSE DESCRIPTION: This course covers different statistical designs, concepts, and procedures that are commonly used in clinical and integrative medicine research. This equips students to understand the statistical rationale and analysis presented in medical literature. They are introduced to basic concepts of probability, random variation, and common statistical probability distributions; and understand the roles of descriptive versus inferential statistics. Students will also understand the different statistical designs, concepts, and analysis.

Course Name: RES601: Biostatistics II (graduate course)
Term: Spring 2027 (Begins April 5^th, 2027 – Ends June 26^th, 2027)
Course Credits: 3 (compensation range: $2340 – $2430)
COURSE DESCRIPTION: In this advanced course, students learn techniques appropriate for handling a single outcome variable and multiple predictors. They develop skills in the use of appropriate statistical procedures for estimation and inference, according to underlying assumptions and type of study design. The interpretation of statistical analysis and understanding the limitations of the data and its consequences will also be discussed. The other component of this course includes developing basic skills for analyzing data using statistical computing software packages.

 

REQUIREMENTS:

Applicants must have education, experience, skill, and knowledge in the area they will be teaching. Master’s degree required; doctoral degree preferred. This course can be taught online, and interviewing will take place online as well.