MJ Lab at Harvard Medical School / MGH

Program Type

SURGH

Organization Name

MJ Lab at Harvard Medical School / MGH

Internship Location

Boston, MA

Organization

The MJ Lab works at the intersection of systems science, simulation modeling, and AI-enabled analytics to study complex health challenges and support decision-making in global health. The lab focuses on problems where evidence is limited, policies interact in unexpected ways, or decision-makers need tools that clarify tradeoffs and anticipate unintended consequences.

Their approach blends systems thinking, stakeholder engagement, causal mapping, data synthesis, and modeling techniques to help partners understand how health systems evolve over time.

The team collaborates with ministries of health, universities, and public health agencies in Ethiopia, Thailand, Canada, and other settings. Projects span infectious diseases, non-communicable diseases, maternal and child health, digital health, health financing reforms, and substance use. They build simulation models and AI-supported scenario tools that allow partners to test strategies before implementation, compare options, and monitor system performance. Outputs include dashboards, decision briefs, and interactive teaching materials.

Across all work, the lab emphasizes transparency, co-design, and capacity building—training partners to interpret, refine, and eventually own the tools developed. The broader goal is to strengthen global health systems by creating methods and tools that support more informed and forward-looking decisions.

Position Overview

This internship offers students hands-on experience working with global health projects that use systems thinking, simulation modeling, and AI-enabled methods to inform policy and planning. Students gain exposure to how analytical tools are built, refined, and used to guide decisions in real health systems.

The intern will support a defined project linked to global health decision-making. Depending on timing and match, potential projects may include:

• A systems modeling study on non-communicable disease care and health financing reforms in Ethiopia
• A simulation or AI-supported analysis of cancer screening pathways or epidemic policy responsiveness with partners in Thailand
• A maternal and child health or infectious disease systems project within existing global partnerships

Across all options, the work involves systems mapping, data synthesis, literature reviews, and contributing to model development or AI-supported tools.

Project Details

• Conduct structured literature reviews and synthesize findings for modeling
• Assist with systems thinking tasks such as causal mapping and organizing qualitative information
• Clean, organize, or explore data to support model inputs
• Help draft components of dashboards, decision briefs, or scenario summaries
• Contribute to simulation modeling or AI-enabled analyses (depending on skill set)
• Support preparation of slides or briefs for stakeholders

Intern Responsibilities

• Support literature reviews and data synthesis
• Contribute to causal mapping and systems diagrams
• Assist with data processing for model inputs
• Participate in development of dashboards or scenario tools
• Join team meetings and collaborate with researchers
• Help prepare short summaries or slide decks for partners and stakeholders

Qualifications

• Interest in global health, health systems, public health, data science, or related fields
• Strong analytical thinking and willingness to learn new methods
• Ability to communicate clearly and collaborate in a research-oriented environment

Learning Outcomes

• Experience applying systems thinking to real global health challenges
• Exposure to simulation modeling and AI-enabled analytical methods
• Understanding how data informs policy options and system behavior
• Insight into how teams translate analytic outputs into decision-making tools
• Experience collaborating with ministries of health and international partners

Nature of Internship

Health Systems Strengthening

Keywords

Research, systems science, modeling, AI-enabled analytics

Additional Onboarding for Selected Intern(s)

Interns must complete standard MGH onboarding to access the building. No IRB requirements. If participating in data-related tasks, students may need to complete the short CITI Responsible Conduct of Research module.

Work Environment

Hybrid. Some lab members work remotely while others are in the office most days. Interns should plan to be in person at least two days per week for collaboration, mentoring, and hands-on project work. Remote participation is possible for certain tasks.