PhD Position – Experiments for Data-Driven Fire Simulation, NYU IgNYte Lab

The IgNYte Lab at NYU Tandon School of Engineering is recruiting one PhD student to start in September 2027 on data-driven fire simulation methods. The student will join the Department of Mechanical and Aerospace Engineering in Brooklyn, New York, under the supervision of Prof. Augustin Guibaud. The lab combines advanced experimental diagnostics with scientific machine learning to make fire predictions more reliable.

Research project

Computational fire models, typically based on large eddy simulation, rely on subgrid-scale closure models for turbulent mixing, turbulence-chemistry interaction, soot formation and oxidation, and radiative heat transfer. The development and validation of these closures is limited by the scarcity of spatially resolved, simultaneous measurements of temperature, velocity, soot volume fraction and radiative heat flux in buoyancy-driven turbulent diffusion flames. Existing datasets are often restricted to time-averaged centerline profiles or to gaseous burners with prescribed fuel flow rates, which decouple the burning rate from the flame’s heat feedback. Liquid pool fires offer a canonical configuration in which the fuel mass loss rate is governed by convective and radiative heat feedback to the fuel surface, tightly coupling flow, radiation and chemistry. This project aims to deliver high-fidelity, multi-diagnostic mappings of the thermal, velocity and soot fields of a well-characterized pool fire, and to leverage them through physics-informed inverse methods to formulate and validate closure models for the next generation of fire simulation tools.

The PhD centers on a new flame spread experiment designed to produce a rich, multi-diagnostic dataset of flame structure and heat transfer. The student will design, build and operate the setup, instrumented with:

  • High-resolution thermocouple measurements of the temperature field
  • Particle image velocimetry (PIV) of the flow field
  • Heat flux measurements to the surroundings and the fuel surface
  • In-situ extinction measurements of soot and smoke

The measurements will then feed the modeling work. Using machine-learning-powered inverse methods, the student will recover the thermal field within the flame from the measured data. These reconstructions will serve to develop and validate closure models for fire simulation tools, bridging laboratory experiments and predictive simulation.

Throughout the PhD, the student will have the opportunity to contribute to other ongoing lab projects in wildfire research, urban firefighting, and spacecraft fire safety. The lab works closely with partners in the fire service, government, industry and international academia.

Who should apply?

  • An MS in mechanical or aerospace engineering, or a closely related field; applicants with a BS in mechanical or aerospace engineering and a GPA of 3.5 or higher may also apply (program requirements)
  • A strong background in fluid mechanics, heat transfer or combustion
  • Enthusiasm for hands-on experimental work in the laboratory
  • Programming skills (e.g., Python) and an interest in machine learning; prior experience is a plus, not a requirement

How to apply?

Interested candidates are encouraged to reach out early: discussions with prospective students will be held throughout November 2026.

  1. Express your interest by email to Prof. Augustin Guibaud at [email protected], with your CV, transcripts and a few lines on your motivation for the project.
  2. Apply to the NYU Tandon PhD program in Mechanical Engineering through the graduate application portal by Dec 1, 2026, the deadline for all PhD applications.

Learn more about the lab, its people and its research at www.ignytelab.org.