Research Fall Internship​/Co-op -Early Anomaly Detection & Intervention

FM is a leading property insurer of the world’s largest businesses, providing more than one-third of FORTUNE 1000-size companies with engineering-based risk management and property insurance solutions. FM helps clients maintain continuity in their business operations by drawing upon state-of-the-art loss-prevention engineering and research; risk management skills and support services; tailored risk transfer capabilities; and superior financial strength. To do so, we rely on a dynamic, culturally diverse group of employees, working in more than 100 countries, in a variety of challenging roles.

Data centers are experiencing unprecedented demands in power density driven by AI and high-performance computing workloads. In contrast to the modern development, traditional fire detection systems are designed to detect fires only after combustion has already occurred, creating a need for earlier anomaly detection methods capable of identifying precursors to thermal runaway, electrical faults, overheating, insulation degradation, arc events, and other incipient fire conditions.

This internship will investigate and evaluate advanced approaches for early anomaly detection and intervention in data center environments. The selected intern will conduct experimental and modeling research to characterize pre-fire signatures and assess potential intervention strategies before ignition or fire development.

• Conduct literature reviews, including internal reports and external publications.
• Execute experimental test programs and collect high-quality data. The focus will be designing and building laboratory-scale experimental apparatus to simulate data center equipment faults and pre-fire conditions.
• Develop models, correlations, and analytical techniques to interpret experimental results.
• Document findings through technical reports, presentations, and research publications.

Location: Norwood, MA
Duration: 3–6 months starting from September 2026
Education Level: Ph.D. student in Fire Protection Engineering, Mechanical Engineering, Chemical Engineering, or related fields.

• Strong experimental and analytical skills.
• Ability to independently design experiments and troubleshoot laboratory setups.
• Experience with data analysis using Python, MATLAB, R, or similar tools.
• Strong technical writing and communication skills.

• Experience in building experimental apparatus and integrating sensors with data acquisition systems.
• Experience with statistical modeling, predictive analytics, or physics-based modeling.

This is an in-office role based in Norwood, MA. Temporary relocation support provided for selected qualified candidate.

FM is an Equal Opportunity Employer and is committed to attracting, developing, and retaining a diverse workforce.