NVIDIA’s AI Factories rely on advanced physics-based digital twins to design, validate, and optimize cooling infrastructure for the next generation of accelerated computing
Requirements
- Solid understanding of thermofluid dynamics, single- and two-phase heat transfer, and data center cooling systems
- Experience with CFD tools such as Cadence, Ansys Fluent, STAR-CCM+, or similar
- Experience with flow network modeling tools such as Flownex
- Strong programming ability in Python
- Experience with USD, SimReady standards, or Omniverse workflows
- Experience in two-phase digital twin modeling familiarity with predictive digital twins or integrating ML/AI with physics-based models
- Experience developing custom Omniverse extensions or simulation automation tools
Responsibilities
- Develop high-fidelity CFD and thermo-fluidic models for single-phase and two-phase cooling systems in data centers, including cold plates, immersion, air cooling, and facility liquid cooling systems
- Build SimReady geometries and metadata for use in NVIDIA’s Omniverse platform using USD-based workflows
- Use Cadence, ANSYS Fluent, STAR-CCM+, Flownex, or equivalent tools to build and validate digital twin components
- Automate simulation workflows using Python, including parameter sweeps, sensitivity studies, multiphysics coupling, and surrogate model generation
- Chip in to multi-domain digital twins spanning thermal, mechanical, electrical, and control systems
- Integrate simulation data with Omniverse extensions and collaborate on R&D-grade digital twin workflows
- Validate simulations with lab and field data and define performance envelopes for cooling technologies
Other
- Pursuing PhD in Mechanical Engineering, Thermal Sciences, Computational Engineering, or closely related field
- Publications or research in thermal management or digital twins
- Intern benefits
- Hourly rate for our interns is 30 USD - 94 USD
- Applications for this job will be accepted at least until December 21, 2025
