The Brown University School of Public Health and the Data Science Institute are pleased to announce an  opening for a tenure-track Assistant Professor with emphasis in Health Data Science. This  interdisciplinary position will have a primary appointment in Biostatistics and a secondary appointment  in the Brown Data Science Institute, and will join a growing and vibrant faculty. In this global search, we  are seeking candidates working at the interface between public health and data science with  demonstrated ability for innovative, high impact research in health data science, broadly defined,  classroom teaching, and student advising and mentoring.  

We seek exceptional candidates who will develop an independent, innovative research program in  statistical methodology, particularly related to deep learning, data fusion, analysis of high-dimensional  and real-world data, and the design and evaluation of AI tools for health. Although we welcome  candidates with broad interests in health-related research, priority domain areas include mental health,  aging, and Alzheimer’s Disease. The School of Public Health, the Data Science Institute, and the broader  University provide outstanding opportunities for collaboration with world-class researchers in  computational mathematics, brain sciences, computational biology, alcohol and addiction research,  gerontology, and health care and policy. The Department of Biostatistics has a rich research portfolio in  causal inference, analysis of high-dimensional and -omics data, Bayesian methods, and clinical trials,  with active collaborations in cancer, aging, HIV, and the analysis of large-scale health data. 

The School of Public Health and the Data Science Institute are responsible for teaching a growing  number of students at the undergraduate, Master’s, and PhD levels. More information about our  educational and training programs can be found on the Brown and DSI websites.

Minimum requirements: Candidates must have a doctoral degree in a methodologic field such as  (bio)statistics, epidemiology, computer science, operations research, industrial engineering, applied  mathematics, or economics. The candidate must have demonstrated excellence in innovative and high impact research in data science (broadly defined) and in classroom teaching and student advising, and  have interest and demonstrated ability for engaging in interdisciplinary public health research. All  candidates must show evidence of excellent oral and written communication skills.  

Please apply online. Review of new applications will begin on  November 15, 2025. Full consideration will be given to all applications received by that date. 

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Applications received after the priority deadline will be reviewed until the position is filled or the search  is closed. 

Interested applicants should submit a letter of application and curriculum vitae through Interfolio. The  letter of application should include a teaching statement, a research statement, and a description of the  applicant’s contributions to data science. Candidates should address how they would contribute to the  research and/or teaching missions of our diverse and inclusive university community. The candidate  should arrange for three letters of reference to be uploaded to Interfolio. 

Potential applicants are invited to contact Jerry Logan (jerry_logan@brown.edu); for additional details  contact the Search Committee Chair Rebecca Hubbard (rebecca_hubbard@brown.edu).

The Data Science Institute at Brown University is seeking a Course Developer and Instructor (adjunct faculty) for a new online Master's in Data Science: Governance, Policy and Society. This is a remote position for an expert who can develop and teach a course on Machine Learning, Deep Learning, and Large Language Models, with a critical focus on the technical aspects of fairness and bias.

About the Role

This position offers a unique opportunity to join a new, innovative graduate program designed to train the next generation of data scientists. The successful candidate will work closely with Brown University's Digital Learning and Design (DLD) team to create a comprehensive, 15-week asynchronous course delivered via the Canvas learning platform. The development phase begins October 1, 2025, and the course will be taught in Summer 2026.

 Key responsibilities include:

• Developing rigorous, student-centered course content that aligns with online teaching and learning best practices.
• Integrating real-world problem sets and applications into the course material.
• Facilitating weekly synchronous sessions to enhance student engagement.
• Managing the course on the Canvas Learning Management System, including grading and student communication.
• Providing consistent academic support and mentorship to students.

The Course

The course offers a comprehensive introduction to machine learning, deep learning, and generative AI. It is distinguished by its unique focus on the technical aspects of detecting and remediating fairness and bias in these models. The curriculum will cover classical machine learning techniques and their susceptibility to bias, as well as the emerging fields of deep learning and large language models (LLMs). Students will explore key concepts such as backpropagation, pretraining, fine-tuning, and alignment strategies like Deep Policy Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF), all with a focus on ethical application.

Qualifications

• A master's degree or higher in Data Science, Statistics, Computer Science, or a closely related field is required.
• A minimum of two years of experience as a Teaching Associate, Adjunct Faculty, or Instructor in a university setting, or advanced expertise in the field gained through professional experience.
• Demonstrated proficiency in the course content, particularly in the technical aspects of ML/DL/LLM fairness and bias.
• Experience in developing and teaching online courses using a Learning Management System like Canvas is preferred.
• A collaborative and self-disciplined individual who can promote an inclusive environment and contribute to a diverse community of perspectives.

Compensation

This position offers a stipend of $14,000, which will be paid in two installments: half at the start of the development phase and half upon completion. The same stipend is awarded each time the faculty member teaches the course. The role requires an average commitment of 5 to 10 hours per week during development.

How to Apply

To be considered for this position, please submit the following materials via this form. 

• A current Curriculum Vitae.
• A cover letter addressing your specific qualifications to teach this unique course, focusing on your expertise in detecting and remediating fairness and bias issues, and the reasons for your interest in the position.
• The names and contact information for three professional references.

Questions can be addressed to amanda_whittaker@brown.edu.