Demo

Research Intern – Machine Learning and Fire Modeling

Factory Mutual Insurance Company
Norwood, MA Intern
POSTED ON 3/7/2025
AVAILABLE BEFORE 3/5/2026

Overview

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.

Responsibilities

Our Research team is seeking a graduate student or advanced undergraduate student in computational science or related fields (e.g. applied mathematics, computer science, mechanical engineering, etc.) to join a team of scientists dedicated to computational modeling of fire dynamics as a summer intern. The project involves developing algorithms and applying scientific machine learning approaches in numerical heat transfer and fluid dynamics problems.

 

Responsibilities:

  • Support a project in the area of scientific machine learning and radiative heat transfer.
  • Develop, integrate, and implement algorithms to address computational issues in combustion and fire scenarios.
  • Present the research outcomes in project meetings and prepare deliverable reports and journal publications.
  •  

    Qualifications

    Doctorate preferred, graduate student or advanced undergraduate student in computational science or related fields (e.g. applied mathematics, computer science, mechanical engineering, etc.)

  • Demonstrated ability to reduce theoretical knowledge and algorithm to practical applications and produce high-quality research results.
  • Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch, JAX) and familiarity with recent research trends in AI for Science and scientific machine learning (e.g., physics-informed neural networks and operator learning).
  • Prior experience developing scientific computing code, computational fluid dynamics code and familiarity with fire protection engineering concept are a plus.
  • Strong analytical, problem-solving, and communication skills
  • 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. 

    #LI-TA1

     

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