"Postdoctoral Research Associate in Biostatistics - PH 351"

Dr. Larry Han, Assistant Professor of Biostatistics and Data Science at Brown University, invites applications for a Postdoctoral Research Associate (100% FTE for a minimum of 2 years) to conduct cutting-edge research at the interface of biostatistics, machine learning, and biomedical data science.

This mentored postdoctoral position is designed to support the development of an independent research trajectory in methodological biostatistics and machine learning, with particular emphasis on robust and trustworthy methods for learning from complex biomedical data. The postdoctoral associate will join an active and collaborative research environment spanning the Department of Biostatistics, the Brown Data Science Institute, and interdisciplinary collaborators at Harvard University, Fred Hutch Cancer Center, and across clinical and public health domains.

Research in this group focuses on developing rigorous biostatistical and machine learning methods for modern biomedical and health data, including randomized trials, electronic health records, claims data, registries, pragmatic clinical studies, and other real-world data sources. Areas of methodological interest may include uncertainty quantification, conformal inference, causal inference, transfer learning, federated learning, data integration, algorithmic fairness, survival analysis, and methods for heterogeneous and multi-source data.

Training Environment and Career Development

The postdoctoral associate will receive structured mentorship and will be supported in:

• Developing an independent line of research in statistical machine learning and data science
• Publishing first-authored manuscripts in leading peer-reviewed journals and conference venues
• Advancing methodological work in areas such as uncertainty quantification, causal inference, data integration, survival analysis, and trustworthy AI
• Preparing competitive career development awards, fellowships, and faculty job market materials
• Contributing to and leading components of federal, foundation, and private grant applications
• Presenting research at national and international scientific meetings
• Building interdisciplinary collaborations across biostatistics, epidemiology, computer science, social sciences, clinical medicine, and public health

The postdoc will have opportunities to engage with faculty, trainees, and collaborators across Brown University and affiliated research networks, including at Harvard University and Fred Hutch Cancer Center and to contribute to a growing ecosystem in data science and health analytics.

Responsibilities

• Conduct original methodological research in statistics, machine learning, epidemiology, and biomedical data science
• Develop and evaluate new methods for causal and predictive inference, and decision-making using large-scale and/or multi-source data
• Collaborate with faculty and research partners on applied and methodological projects
• Lead and contribute to peer-reviewed publications
• Assist with preparation of grant applications and related scientific materials
• Present research findings to academic and interdisciplinary audiences
• Participate actively in the intellectual life of the Department of Biostatistics and the Brown Data Science Institute

Appointment Details

The initial appointment is for 2 years, with potential renewal contingent upon performance and funding availability. Salary is competitive and commensurate with experience and includes a comprehensive benefits package.

Visas

For this position, Brown offers only the J-1 visa classification to scholars who need immigration sponsorship in order to enter the U.S. and commence lawful employment under the terms of their appointment.

PhD or equivalent doctoral degree in biostatistics, statistics, computer science, data science, epidemiology or a related quantitative field by the start date.

The successful candidate will demonstrate:

• Strong methodological training in (bio)statistics and/or machine learning
• Interest in developing rigorous methods for biomedical, clinical, or public health data
• Experience with statistical computing (R and/or Python) and reproducible research workflows
• A strong publication record relative to career stage
• Excellent scientific writing and communication skills
• Ability to work effectively in interdisciplinary research settings

Experience with one or more of the following is preferred:

• Electronic health records, claims data, registries, or other real-world health data
• Causal inference, conformal inference, federated learning, transfer learning, or fairness-aware machine learning methods
• Collaborative work with applied investigators in health-related domains

Applicants should submit:

• Cover letter describing research experience, career goals, interest in the position, and plans for pursuing independent funding. Candidates should also address how they would contribute to the research and/or teaching missions of Brown’s diverse and inclusive academic community.
• Curriculum vitae
• One representative publication
• Names and contact information for three references

Applications should be submitted online via Interfolio.