Health Informatics Postdoc | Library Data Science

Binghamton University, State University of New York

City of Binghamton (NY)

On-site

USD 60,000 - 82,000

Full time

14 days+
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Job summary

Binghamton University Libraries is seeking a Postdoctoral Associate focused on Health Informatics. The 12-month, full-time position supports evidence-based practice, data analysis, and local workflows to strengthen the university’s research program.

The role enables collaboration with Guthrie Clinic under a new agreement. The ideal candidate will pursue an active research agenda, apply machine learning to health data, and contribute to teaching or guest lectures as part of a mentored

Qualifications

  • PhD within the last 5 years in Information and Library Science or a closely related field.
  • Demonstrated research experience in evidence synthesis, systematic review, data curation, health informatics, or related area.
  • Effective written and oral communication skills, with the ability to convey complex information clearly and develop productive professional partnerships.

Responsibilities

  • Supporting health data synthesis using available Guthrie Clinic and related data sets.
  • Organizing and analyzing large data sets, data modeling and forecasting and designing or applying machine learning approaches to support reliable research outcomes and communicating findings.
  • Collaborating with faculty and interdisciplinary partners, and contributing as a guest lecturer or instructional collaborator.

Skills

Effective communication
Programming languages
Quantitative skills

Education

PhD in Information and Library Science or related field

Job description

Binghamton University Libraries is seeking a Postdoctoral Associate focused on Health Informatics. The 12-month, full-time position supports evidence-based practice, data analysis, and local workflows to strengthen the university’s research program.

The role enables collaboration with Guthrie Clinic under a new agreement. The ideal candidate will pursue an active research agenda, apply machine learning to health data, and contribute to teaching or guest lectures as part of a mentored

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