Simulation Engineering Intern - Fire CFD

About us

Please only apply to one internship position you feel most aligned with. 

If we think you are better suited for another, we will communicate this to you.

Our internship opportunities are designed for students who want to apply what they’ve learned in their degrees to real engineering problems. You’ll work alongside simulation engineers, data scientists, machine learning engineers and research scientists on our projects, gaining hands-on experience and insight into how physical AI is developed and used in industry. 

Who We're Looking For

As a Simulation Engineering Intern for fireAI group, you will join us over summer 2026 to develop automated CFD simulation workflows and generate high-fidelity datasets for machine learning applications in computational fire dynamics for AI-driven fire behavior predictions. This exciting opportunity will allow you to build end-to-end automation pipelines for fire dynamics using CFD, working with emerging fire hazards in energy storage and transportation infrastructure. Your work will be instrumental in creating datasets that power PhysicsX's next-generation AI-driven fire prediction tools, serving rapidly growing markets where fire hazard analysis is both a regulatory requirement and a critical design consideration.

You will gain hands-on experience working at the intersection of fire modeling and engineering, high-performance computing, and machine learning, while contributing to cutting-edge research that advances the field of computational fire modeling. Join us in this dynamic role, where your expertise will help establish critical fire modeling capabilities and push the boundaries of innovation in AI-driven fire safety analysis.

What you will do

You will engage in the following activities and deliver key milestones throughout the internship:

• Develop programmatic geometry generation workflows for complex layouts with parametric variation in configurations, spatial arrangements, ventilation systems, and structural elements
• Build automated simulation generation pipelines implementing design-of-experiments strategies to explore diverse fire scenarios including ignition locations, heat release rates, fire propagation patterns, and suppression system responses
• Configure and manage large-scale simulation campaigns on cloud HPC infrastructure, including batch job submission, and monitoring workflows for parallel simulations
• Implement automated post-processing routines to extract key fire safety metrics including temperatures, smoke characteristics, toxic gas concentrations, heat flux distributions, and time-based egress calculations
• Collaborate with data scientists and machine learning engineers to structure simulation outputs for training datasets, understand data quality requirements, and participate in model validation workflows
• Generate high-fidelity fire modeling datasets spanning diverse configurations to support ML surrogate model development, working closely with data scientists and machine learning engineers on model architecture, hyperparameter optimization, and validation strategies to ensure accurate AI-driven fire behavior predictions
• Research and validate simulation methodologies by reviewing technical literature on fire modeling, documenting material properties, benchmark studies, and relevant fire safety codes and standards
• Develop comprehensive technical documentation explaining automation pipelines, fire modeling approaches, underlying physics being simulated, and references to literature and industry standards
• Contribute to potential publication of research findings in peer-reviewed journal paper or PhysicsX internal publication, documenting methodologies and insights from fire modeling dataset generation and AI-driven models

What You Bring To The Table

Required:

• Currently pursuing a PhD (or Masters) degree in Mechanical Engineering, Civil Engineering, Aerospace Engineering, Fire Protection Engineering, or related engineering field
• Strong experience with computational fluid dynamics software (CFD) and fire modeling tools
• Proficiency in Python programming for automation, data processing, and workflow orchestration
• Coursework or demonstrated knowledge in fire dynamics, heat transfer, fluid mechanics, and combustion
• Experience with Linux/Unix operating systems and command-line scripting
• Strong problem-solving skills and ability to work independently on technical challenges

Preferred:

• Prior experience with CFD, and fire simulations using Fire Dynamics Simulator (FDS), ANSYS Fluent, Star CCM+, and/or OpenFOAM, and experience with visualization tools such as SmokeView, ParaView, and Blender
• Knowledge of fire behavior physics, thermal dynamics, and suppression strategies
• Familiarity with fire safety codes (IBC, IFC), standards (NFPA, SFPE), tests and research studies (FM Global, NIST, RISE, etc.)
• Experience applying data science and machine learning methods to real-world engineering applications, with a focus on driving measurable impact in industry settings
• Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, PyTorch, MLFlow) (note: no prior ML experience required)
• Experience with HPC job scheduling systems and cloud computing platforms
• Background in parametric design automation, CAD scripting, or mesh generation

Salary: 

The estimated range for this position is $45 - 55 USD per hour. The total compensation will be determined by each individual's relevant qualifications, skills and work experience. 

Applications will close 10 February 2026.