POSTDOCTORAL ASSOCIATE

School of Medicine

Established in 1930, Duke University School of Medicine is the youngest of the nation's top medical schools. Ranked sixth among medical schools in the nation, the School takes pride in being an inclusive community of outstanding learners, investigators, clinicians, and staff where interdisciplinary collaboration is embraced and great ideas accelerate translation of fundamental scientific discoveries to improve human health locally and around the globe. Composed of more than 2,600 faculty physicians and researchers, nearly 2,000 students, and more than 6,200 staff, the Duke University School of Medicine along with the Duke University School of Nursing, and Duke University Health System comprise Duke Health, a world-class academic medical center. The Health System encompasses Duke University Hospital, Duke Regional Hospital, Duke Raleigh Hospital, Duke Health Integrated Practice, Duke Primary Care, Duke Home Care and Hospice, Duke Health and Wellness, and multiple affiliations.

Position Overview

We are seeking a highly motivated postdoctoral researcher with expertise in artificial intelligence and machine learning (AI/ML) to join our interdisciplinary team at Duke University. The postdoc will contribute to developing and evaluating state-of-the-art methods for predicting mental health outcomes from multi-modal clinical and digital health data. This position offers the opportunity to work within a vibrant research community dedicated to building trustworthy, interpretable, and clinically actionable AI for next-generation clinical decision support.

Research Focus

The successful candidate will lead methodological innovation and applied research in predictive modeling for mental health, drawing on diverse data modalities such as:

• Electronic Health Records (EHR)
• Patient-reported outcomes (PROs)
• Neuroimaging and biomarker datasets

Core methodological areas of interest include:

• Uncertainty quantification for individual-level predictions
• Methods for distribution shift (e.g., domain adaptation, dataset shift robustness)
• Causal inference for risk prediction and decision support
• Fairness and trustworthy AI in healthcare applications
• Multi-modal representation learning and integration of heterogeneous data

Responsibilities

• Design, implement, and evaluate new AI/ML methods for mental health prediction.
• Develop and test models that address uncertainty, fairness, and robustness to real-world clinical settings.
• Collaborate with psychiatrists, psychologists, biostatisticians, and data scientists to ensure clinical and translational relevance.
• Lead preparation of manuscripts, presentations, and contribute to grant proposals.
• Mentor graduate students and research assistants working on related projects.
• Engage with Duke AI Health and the broader academic/clinical community to advance responsible AI in healthcare.

Qualifications

Required:

• PhD (or equivalent) in computer science, statistics, biostatistics, electrical/biomedical engineering, or related quantitative field.
• Strong background in machine learning, deep learning, or statistical modeling.
• Demonstrated experience working with clinical, digital health, or related biomedical data.
• Proficiency in Python, R, or other scientific programming languages.

Preferred:

• Research experience in one or more of:
• Uncertainty quantification
• Methods for distribution shift
• Causal inference
• Fairness and trustworthy AI
• Highly proficient in Python and Pytorch
• Experience with multi-modal data integration, including patient-reported outcomes.
• Strong publication record in AI/ML or biomedical informatics.
• Interest in translational mental health research and interdisciplinary collaboration.

Training & Career Development

The postdoctoral researcher will receive mentorship in advanced methodological development, grant writing, and career planning, with opportunities to collaborate across Duke AI Health, the Department of Biostatistics & Bioinformatics, and the Department of Psychiatry & Behavioral Sciences. The position provides support for pursuing independent funding (e.g., K99/R00) and building an independent research program.

Application Instructions

Applicants should submit:

1. A cover letter describing research interests, methodological expertise, and career goals.
2. Curriculum vitae.
3. Contact information for three references.

Applications will be reviewed on a rolling basis until the position is filled.