Position: Adjunct Instructor

Course: GPH-GU 2338/3338 Machine Learning in Public Health ( Syllabus) (

Department: NYU School of Global Public Health - Biostatistics

Supervisor: Dr. Rebecca Betensky

Employment Dates: Spring 2026

This course will provide students with a comprehensive understanding of machine learning and its applications in public health and biomedicine. Topics covered include the data generating process, model selection and evaluation, generalized linear models, various supervised and unsupervised machine learning algorithms (such as support vector machines, decision trees, random forests, neural networks, and k-means), and ethical considerations in machine learning.

Students will learn how to implement machine learning methods effectively, including the assessment of assumptions about the data-generating process, the creation of relevant features, the handling of missing data, and the reduction of bias. In addition to gaining familiarity with the potential power of machine learning in public health, students will also explore the specific challenges and limitations inherent to these applications. By the end of the course, students will have a solid foundation in machine learning and its potential for advancing public health research and practice.

Qualified applicants will have an advanced degree in statistics or a related field (PhD preferred). Previous experience teaching introductory statistics is preferred.

If interested, please upload a CV and cover letter via Interfolio.

In compliance with NYC's Pay Transparency Act, the hourly rate for this position is $222.27. New York University considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/training, key skills, internal peer equity, as well as market and organizational considerations when extending an offer.

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