Adjunct Faculty (Open Track, Open Rank), School of Public Health, Online Masters in Biostatistics (
Brown University School of Public Health
Application
Details
Posted: 07-Feb-25
Location: Providence, Rhode Island
Type: Full Time
Categories:
Applied Statistics
Biometrics/biostatistics
Mathematical statistics
Preferred Education:
Doctorate
Brown University’s new Online Masters in Biostatistics (Health Data Science Track) in the School of Public Health (SPH) aims to open opportunities for new types of learners and train more of tomorrow’s leaders in Biostatistics to improve the health of people domestically and around the world. The Online Biostatistics Program is geared toward non-traditional adult learners, working professionals, and international students. The extension of the Brown Biostatistics Masters program to online students fortifies SPH’s commitment to expanding the socioeconomic and racial/ethnic diversity of enrolled students and embodies the University’s mission to serve the community, nation, and world.
Adjunct faculty are required to report to the Department Chair and Academic Director throughout the time that they are administering course(s) for the Online Biostatistics Program. We are looking for faculty who have the expertise to develop and teach the following courses:
Statistical Machine Learning: This course covers three key areas: statistical machine-learning methods, underlying algorithms, and computational tools. Students will explore statistical learning methods, including classification, regularized regression, and clustering, with a focus on predictive modeling and scalable methods for large health datasets (e.g., claims, EHRs, genomic sequencing). The curriculum also includes modern machine learning tools, such as boosting and ensemble methods, for extracting complex patterns from big data.
Advanced Topics in Health Data Science: This course explores the theories and applications of statistical, computational, and machine learning methods to analyze and interpret complex health data and make predictions. Students also will learn the concepts of generative predictive models (such as large language models (LLMs) and transformers (GBT)). The course emphasizes practical skills, with a focus on working with electronic health records, genomics, and other high-dimensional data, to address challenges in healthcare research and decision-making.
Adjunct faculty will be expected to develop and facilitate online Biostatistics course(s) to optimize student engagement as well as serve as a supportive resource for students’ academic concerns. Adjunct faculty will work directly with the Department Chair and Academic Director, consult with certain Biostatistics faculty, and work closely with the Sheridan Center for Teaching and Learning Online Team to design and develop student-centered online Biostatistics course(s) based on prescribed program objectives and course syllabus, the required content, and assignments for each course six months in advance of the course launch. Faculty will also implement the resulting asynchronous online course that is student-centered, media-rich, and objective-driven in accordance with Department of Education regulations. The course(s) are geared for reusability and scalability, and emphasis is placed on consistency of content, scope and rigor, implementation of best practices, and outcomes-based learning approaches. Brown University values the disciplinary competency that faculty bring to the course and to students through feedback and insight provided. The Sheridan Center’s Online team is made up of an instructional designer, educational media producer, and learning technologist who will meet weekly with the adjunct faculty member to help develop the course(s).
Each adjunct must be highly knowledgeable about the subject matter for the specific course(s) that they are assigned to develop and facilitate, maintain expertise in the subject area, and support student success during the administration of the course. The curriculum of the Online Biostatistics program includes: probability and principles of statistical inference, statistical programming, regression analysis (generalized linear models, longitudinal data analysis, hierarchical modeling, etc) and machine learning methods, observational studies and clinical trials design, advanced topics in health data science, and capstone projects. All courses will be delivered completely online, combining predominantly asynchronous delivery with regularly scheduled synchronous components. The adjunct will support student learning in both components.
The new faculty member will also become a member of a highly interdisciplinary Biostatistics faculty in the Brown University School of Public Health. Building on its methodologic and domain research strength in the analysis of large health care databases, causal inference, diagnostic imaging evaluation and radiomics, computational biology and bioinformatics, Bayesian methodology, research synthesis, and neuroscience, the Department is now emphasizing growth in research and educational activity in health data science. Biostatistics is a core member of the University’s Data Science Initiative.