Postdoctoral Research Associate

About the role

The Department of Statistics at the College of Liberal Arts and Sciences, University of Connecticut (UConn), invites applications for a full-time Postdoctoral Research Associate position. The successful candidate will engage in innovative research addressing statistical methodology, machine learning, and/or learning techniques in complex biomedical and health-related challenges. We particularly focus on real-world applications in psychology, psychiatry, mental health, aging, and related fields. The Postdoctoral Research Associate will work under the mentorship of Dr. Ofer Harel and will potentially collaborate with his collaborators across the university and the medical and dental schools.

Responsibilities

• Conduct innovative research addressing statistical methodology, machine learning, and/or learning techniques in complex biomedical and health-related challenges
• Focus on real-world applications in psychology, psychiatry, mental health, aging, and related fields
• Collaborate with Dr. Ofer Harel and his collaborators across the university and the medical and dental schools

Minimum qualifications

• Ph.D. in Statistics, Biostatistics, Computer Science, Data Science, Information Science, or a closely related field (completed by start date)
• Demonstrated research experience in statistical/mathematical foundations relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods)
• Programming skills in Python and/or R, with evidence of producing well-structured, user-friendly, reproducible research code (e.g., packages, documented pipelines, open-source contributions, or similar)
• Written and oral communication skills, with the ability to work effectively in an interdisciplinary team

Preferred qualifications

• Experience with one or more of the following: knowledge graphs, graph machine learning, link prediction, representation learning/embeddings, probabilistic graphical models, or causal/relational inference
• Background or strong interest in meta-research / scientometrics / scholarly communication, including citation-based analysis, claim/evidence modeling, or evaluation of research impact and novelty
• Experience working with large-scale scholarly datasets and infrastructure (e.g., corpus construction, entity resolution, graph databases, text-mining/NLP pipelines)
• Experience developing research software that supports end users, including building web-based tools/platform features, APIs, dashboards, and deployable prototypes (level of depth can vary)
• Experience designing or using AI-assisted research workflows (e.g., LLM-supported literature synthesis, structured extraction of claims/evidence, reproducible AI-in-the-loop pipelines, or evaluation/verification of model-generated outputs)
• Evidence of strong collaborative instincts and experience engaging with diverse faculty, students, and research stakeholders

Appointment terms

• Full-time, 12-month position
• May be renewed annually based on performance and availability of funds
• Anticipated 2-3 years of funding to support this position
• Start as soon as possible, with the latest start date of fall 2026