Postdoctoral Fellowship in Smart Health, Biostatistics, and AI at UCLA
The Department of Biostatistics at the University of California, Los Angeles (UCLA) is offering a postdoctoral fellowship in Smart Health, focusing on the intersection of biostatistics, artificial intelligence, and public health. The position is supervised by Dr. Jin Zhou, Dr. Hua Zhou, and Dr. Gang Li, and is ideal for candidates with a strong background in statistics, machine learning, and data science, particularly as applied to wearable devices, electronic health records, and statistical genetics at the scale of biobanks.
The research themes include high-dimensional causal mediation and inference using AI methods, as well as AI and time series modeling for wearable device data and other longitudinal health data. The postdoc will develop and apply modern causal and statistical learning approaches to complex, high-dimensional data such as multi-omics, imaging, and rich clinical covariates, with a focus on understanding mechanisms linking exposures, mediators, and health outcomes in chronic diseases like diabetes. Another focus is on designing and evaluating AI-informed methods for long sequence modeling and forecasting of physiological signals, integrating these data with electronic health records to support risk prediction, decision support, and adaptive interventions.
The successful candidate will join an interdisciplinary team and have substantial protected time for methodological research, opportunities to work with rich real-world datasets, and the chance to contribute to collaborative papers and grant proposals. Responsibilities include developing, implementing, and evaluating new statistical and machine learning methods, leading and co-authoring manuscripts, collaborating with clinical and scientific partners, and presenting work at conferences and seminars.
Applicants must hold a PhD (or be near completion) in statistics, biostatistics, computer science, applied mathematics, or a related quantitative field. Required skills include a strong foundation in probability, statistical modeling, and AI/machine learning, with demonstrated interest in causal inference, high-dimensional data analysis, time series or sequential modeling, or deep learning for structured data. Experience with real data analysis in R or Python is expected, and familiarity with health, biomedical, or sensor data is a plus. Excellent communication skills and the ability to work in interdisciplinary teams are essential.
The position offers a competitive salary ($69,073–$82,836 per year) and is fully funded. The application window opens December 19, 2025, with full consideration given to applications received by January 6, 2026. Interested candidates should apply online, submitting a CV and optional cover letter and statement of research, and complete the reference check authorization release form. For more information, visit the official UCLA recruitment page or contact [email protected].
