AI Factory Digital Twin R&D Intern, Single & Two-Phase Cooling Systems - Summer 2026
About The Position
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. We are seeking a PhD-level intern to drive R D in single-phase and two-phase cooling digital twin development, creating high-fidelity SimReady assets and contributing to the next wave of intelligent AI Factory infrastructure. What You Will Be Doing: 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. Work with the entire NVIDIA Data Center Engineering (DCE) team across cooling, power, controls, and architecture. Validate simulations with lab and field data and define performance envelopes for cooling technologies.
Requirements
- Pursuing PhD in Mechanical Engineering, Thermal Sciences, Computational Engineering, or closely related field.
- 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.
Nice To Haves
- 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.
- Publications or research in thermal management or digital twins.
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.
- Work with the entire NVIDIA Data Center Engineering (DCE) team across cooling, power, controls, and architecture.
- Validate simulations with lab and field data and define performance envelopes for cooling technologies.
Benefits
- Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience.
- The hourly rate for our interns is 30 USD - 94 USD.
- You will also be eligible for Intern benefits .
- NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
Number of Employees
5,001-10,000 employees
