Postdoctoral Fellowship in Smart Health

Dr. Jin Zhou, Dr. Hua Zhou, and Dr. Gang Li of the Department of Biostatistics at UCLA are actively seeking a dedicated postdoctoral fellow with robust expertise in AI and statistics with applications to wearable devices, electronic health records, and statistical genetics at the scale of biobanks. The position will focus on methodological and applied work in either one of the two broad, complementary areas:

• High-dimensional causal mediation and inference using AI methods
  
  Developing and applying modern causal and statistical learning approaches for complex, high-dimensional data (e.g., multi-omics, imaging, rich clinical covariates) to understand mechanisms linking exposures, mediators, and health outcomes, with the application to chronic diseases such as diabetes and its complications.

• AI and time series modeling for wearable device data and other longitudinal health data

• Designing and evaluating AI-informed methods for long sequence modeling and forecasting of physiological signals (e.g., continuous glucose), integrating these data with electronic health records and other clinical information to support risk prediction, decision support, and adaptive interventions.

The postdoc will join an interdisciplinary team spanning biostatistics, data science, and clinical collaborators. The successful candidate will have substantial protected time for methodological research, as well as opportunities to work with rich real-world datasets and to contribute to collaborative papers and grant proposals.

Responsibilities

* Develop, implement, and evaluate new statistical and machine learning methods aligned with the two themes above.

* Lead and co-author manuscripts in statistical, machine learning, and clinical journals.

* Work closely with clinical and scientific collaborators to translate methods into applied analyses.

* Present work at conferences and internal seminars; contribute to a collaborative, inclusive research environment.

* PhD (or near completion) in statistics, biostatistics, computer science, applied mathematics, or a related quantitative field.

* Strong foundation in probability, statistical modeling, and AI/machine learning.

* Demonstrated interest in at least one of: causal inference/mediation, high-dimensional data analysis, time series or sequential modeling, or deep learning for structured data.

* Highly motivated to learn new technologies, willing to acquire domain knowledge, and eager to develop engineering skills.

* Experience with real data analysis in R or Python; familiarity with health, biomedical, or sensor data is welcome but not required.

* Excellent written and oral communication skills and ability to work effectively in interdisciplinary teams.

Interested candidates are encouraged to reach out with their credentials and references. We look forward to fostering an environment of growth, innovation, and collaboration.

As a University employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, state, or local government directives may impose additional requirements.

The University of California is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected status under state or federal law.

As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct.

• "Misconduct" means any violation of the policies or laws governing conduct at the applicant's previous place of employment, including, but not limited to, violations of policies or laws prohibiting sexual harassment, sexual assault, or other forms of harassment, discrimination, dishonesty, or unethical conduct, as defined by the employer.
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