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Public Health Internship (Fall 2026)

Lincoln, NE

$15/hr

Public health is the science and practice of protecting and improving the health of people and ... Epidemiology - Data analysis, disease surveillance, and outbreak investigation support * Health ...

JP Systems, Inc.

Healthcare Data Analyst Sr.

VA · Remote

$88K - $111K/yr

Our mission is to standardize, manage and improve the usability of clinical data for Public Health. Data Science and Bioinformatics Team - Healthcare Data Analyst Sr. Responsibilities of the Health ...

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How much do internship public health data science jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for internship public health data science in the United States is $22.50, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $24.52 per hour, depending on experience, location, and employer.

What types of projects or tasks can I expect to work on during a Public Health Data Science internship?

As a Public Health Data Science intern, you can expect to work on projects involving data collection, cleaning, and analysis of health-related datasets. You may assist in developing dashboards, creating visualizations, or supporting research studies that inform public health decisions. Collaboration with epidemiologists, statisticians, and other data scientists is common, providing valuable experience in interdisciplinary teamwork. Interns often have opportunities to present findings and contribute to reports or publications, which can enhance both technical and communication skills.

What are the key skills and qualifications needed to thrive as an Internship Public Health Data Science, and why are they important?

To thrive as an Internship Public Health Data Science, you need a solid understanding of epidemiology, statistics, and data analysis, typically supported by coursework or a degree in public health, statistics, or a related field. Familiarity with statistical software such as R, SAS, or Python, as well as experience with data visualization tools and public health databases, is highly valuable. Strong communication, problem-solving, and teamwork skills help interns effectively interpret data and collaborate with multidisciplinary teams. These skills are essential for accurately analyzing public health data, generating actionable insights, and contributing to evidence-based decision-making.

What is an Internship in Public Health Data Science?

An Internship in Public Health Data Science is a temporary position designed for students or recent graduates to gain hands-on experience in applying data science techniques to public health issues. Interns typically work with large health datasets, conduct statistical analyses, create data visualizations, and assist with research projects aimed at improving public health outcomes. These internships provide practical skills and exposure to real-world challenges in the intersection of data science and public health, preparing individuals for careers in epidemiology, biostatistics, or health informatics.
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Postdoctoral Fellow in Biostatistics & Health Data Science

Indiana University Academic Positions

Kokomo, IN

$42K - $58K/yr

Other

Posted 26 days ago

Job description

Position Details
Title Postdoctoral Fellow in Biostatistics & Health Data Science Specific Title Appointment Type Postdoctoral Fellow Department IUSM - Biostatistics Campus IU School of Medicine Indianapolis Position Summary
Research Context & Opportunity-
Modern healthcare increasingly depends on integrating data across hospitals, registries, cohorts, and public health systems. Yet semantic heterogeneity-differences in terminology, structure, and logic-remains a central barrier to reusability, interoperability, and reproducibility.
This postdoctoral position addresses a fundamental and timely research question:
How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization?

We are particularly interested in:
  • LLM-driven systems for aligning real-world health data to standards like OMOP CDM, FHIR, and UMLS
  • Agent-based workflows that explain, refine, and adapt semantic mappings over time
  • Hybrid architectures that combine knowledge-grounded reasoning with flexible machine learning
  • Tools that reduce manual burden while preserving traceability and clinical interpretability
This position offers the opportunity to publish novel methods, work with real messy multi-source data, and contribute to infrastructure supporting population-level research and health equity.
The postdoctoral fellow will be based in the Department of Biostatistics and Health Data Science at Indiana University School of Medicine, in close collaboration with the Regenstrief Institute, a nationally renowned center for health informatics research and real-world data infrastructure.
  • Indiana University is home to one of the largest medical schools in the U.S., with extensive collaborations across informatics, clinical departments, and health systems (IU Health, Eskenazi Health, etc.).
  • Regenstrief Institute is internationally recognized for its leadership in data standards (e.g., LOINC), clinical data networks (e.g., Indiana Network for Patient Care), and health information infrastructure.
  • This environment supports both methodological research and operational implementation across state, national, and multi-institutional networks (e.g., NIH, OHDSI, PCORnet, ACT Network).
Our Team's Approach-
We are not a pure research group. We operate at the interface of research and health data operations, building methods that not only publish but also deploy. We handle real clinical and public health data problems where ambiguity, variation, and scale are the norm-not the exception.
We welcome postdocs who want to drive innovation while engaging deeply with practical, meaningful data challenges.
Responsibilities

  • Design and implement LLM-based methods for clinical data harmonization, semantic normalization, and ontology alignment
  • Develop multi-agent or RAG-style (retrieval-augmented generation) workflows for schema matching and terminology mapping
  • Collaborate with national and multi-institutional initiatives in data integration and standardization
  • Support open-source tooling, reproducible pipelines, and standards-based approaches (e.g., OMOP, FHIR, UMLS)
  • Lead or support manuscript preparation and dissemination at top informatics and AI venues
  • Contribute to grant development and proposal writing

What We Offer-
  • A collaborative environment at the intersection of real-world data, applied AI, and translational science
  • Opportunities to work across academic, clinical, and public health settings
  • Mentorship and support toward independent research or career development in academia or industry
  • Competitive salary and benefits through Indiana University
  • A culture that values both scientific innovation and practical impact

The Indianapolis Campus is the focal point of health professions education at Indiana University, and the School of Medicine is the country's second largest allopathic medical school. Indianapolis consistently ranks high nationally on many of the "best places to live" lists and has an economy that is growing in the life sciences arena. In addition, it has always been one of the cities with the lowest cost of living. Carmel, Indy's northern neighbor, was recently named as the best mid-sized city in the country.
IUSM is committed to being a welcoming campus community and we seek candidates whose research, teaching, and community engagement efforts contribute to robust learning and working environments for all students, staff, and faculty. We invite individuals who will join us in our mission to improve health equity and well-being for all throughout the state of Indiana.
Indianapolis is the capital and most populous city in the State of Indiana. It is growing economically thanks to a strong corporate base anchored by the life sciences. Indiana is home to one of the largest concentrations of health sciences companies in the nation. Indianapolis has a sophisticated blend of charm and culture with a wonderful balance of business and leisure. The growing residential base is supported by rich amenities and quality of life - the city possesses a variety of professional sports, arts venues and outdoor recreation areas. Residents of this dynamic city, and surrounding suburbs, enjoy leading educational systems and top-ranked universities, paired with a diverse population. Indianapolis International Airport is a top-ranked international airport, being named "Best Airport in North America" by Airports Council International for many years. For additional information on life in Indy: https://faculty.medicine.iu.edu/relocation. The search will continue until the positions are filled. 
Basic Qualifications
Required Qualifications:
  • Ph.D. (by start date) in Computer Science, Biomedical Informatics, Health Data Science, Biostatistics, or a closely related area.
  • Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP.
  • Demonstrated working experience with healthcare data (e.g., EHR, clinical text, imaging, omics).
  • Proficiency in Python and ML tooling (e.g., PyTorch, scikit-learn), version control (Git), and experiment tracking (e.g., Weights & Biases).
  • Excellent written and oral communication skills, and ability to collaborate with multidisciplinary teams.
Department Contact for Questions
Professor Jiang Bian via email at: bianj@regenstrief.org
Additional Qualifications
Preferred Qualifications:
  • Experience with concept normalization, ontology mapping, or schema alignment
  • Familiarity with LLM agents, tool-augmented reasoning, or hybrid rules + LLM systems
  • Record of publications in relevant domains (informatics, machine learning, AI, knowledge representation)
  • Experience with multi-site data harmonization or federated data environments
Special Instructions Priority Application Review Deadline Expected Start Date Posting Number IUSM-02358-2026

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