About this position
- Search #: 499493
- Work type: Full-time
- Location: UConn Storrs
- Categories: Postdoctoral Research Associates
JOB SUMMARY
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. We seek candidates with strong analytical, computational, and communication skills. While a background in medical imaging or electronic health records (EHR) is beneficial, it is not required.
The University of Connecticut is ranked first among public universities in New England and in the top 20 nationwide. It is located in northeastern Connecticut, with easy access to Boston and New York City. The University’s Department of Statistics is a vibrant, collaborative environment that will provide exposure to a vast array of research areas.
Information for the Department of Statistics, including Dr. Harel’s background and research interests, can be found at statistics.uconn.edu .
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
This is a full-time, 12-month position and may be renewed annually based on performance and availability of funds. It is anticipated that there will be 2-3 years of funding to support this position. The incumbent will start as soon as possible, with the latest start date of fall 2026.
Other rights and terms and conditions of your appointment are contained in the collective bargaining agreement between the University of Connecticut and the International Union, United Automobile, Aerospace, and Agricultural Implement Workers of America (UAW), Local 6950.
TERMS AND CONDITIONS OF EMPLOYMENT
Employment of the successful candidate is contingent upon the successful completion of a pre-employment criminal background check.
TO APPLY
Please apply online at Faculty and Staff Positions , Search #499493 to upload a resume , cover letter, and the contact information for three (3) professional references . Two reference letters may be requested later. For questions, please contact Mackenzie Murphy ( Mackenzie.murphy@uconn.edu ).
Review of applications will begin on October 23, 2026 , and continue until the position is filled.
All employees are subject to adherence to the State Code of Ethics .
All members of the University of Connecticut are expected to exhibit appreciation of, and contribute to, an inclusive, respectful, and diverse environment for the University community.
The University of Connecticut aspires to create a community built on collaboration and belonging and has actively sought to create an inclusive culture within the workforce. The success of the University is dependent on the willingness of our diverse employee and student populations to share their rich perspectives and backgrounds in a respectful manner. This makes it essential for each member of our community to feel secure and welcomed and to thoroughly understand and believe that their ideas are respected by all.
We strongly respect each individual employee’s unique experiences and perspectives and encourage all members of the community to do the same. All applicants will receive consideration for employment without regard to race, color, ethnicity, religion, age, sex, marital status, national origin, ancestry, sexual orientation, genetic information, physical or mental disabilities, veteran’s status, status as a victim of domestic violence and/or sexual assault and/or trafficking in persons as defined by Connecticut law, prior conviction of a crime, workplace hazards to the reproductive systems, or gender identity or expression .
The University of Connecticut is an AA/EEO employer including for Disability and Veteran status.
Advertised: Sep 01 2026 Eastern Daylight Time
Applications close: Oct 23 2026 Eastern Daylight Time
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How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Email the contact below with your CV and a short, specific message. See Emailing Professors.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.