
Advanced CFD & Multiphysics Simulation Engineer
Required skills
- CFD
- Multiphysics
- ANSYS Fluent
- conjugate heat transfer
- internal flow
- pressure drop
- meshing
- convergence
- model verification
- Python
Nice to have
- liquid cooling
- HPC
- AI accelerators
- electronics packaging
- high-power-density systems
Role summary
This role focuses on building the simulation capability for next-generation liquid-cooling solutions for AI, HPC, and data-center infrastructure. The engineer will integrate CFD, thermal design, CAD, optimization, manufacturing, and experimental validation to address high-power-density cooling challenges and develop scalable products. Responsibilities include owning simulation methods, automation, defining design rules, and providing customer-facing technical recommendations, ultimately guiding design decisions with physics-based insights and quantified confidence.
Summary:
Build the simulation capability behind next-generation, manufacturable liquid-cooling solutions for AI, HPC, and data-center infrastructure.
This role will expand Rogers' advanced manufacturing capability and data-center cooling portfolio. The engineer will connect CFD, thermal design, CAD, optimization, manufacturing, and experimental validation to convert high-power-density cooling challenges into scalable products. The position offers growth beyond conventional CFD analysis through ownership of simulation methods, automation, design rules, and customer-facing technical recommendations. Success means recommending which design should advance, explaining the governing physics and trade space, quantifying manufacturing sensitivity and model confidence, and defining the experiment needed to validate the decision.
Essential Functions:
• Perform CFD, conjugate heat-transfer, thermal, and Multiphysics analyses for liquid-cooled electronics, with strong emphasis on ANSYS Fluent.
• Develop models for conduction, convection, pressure loss, flow maldistribution, manifolds, interfaces, material behavior, cold plates, microchannels, heat spreaders, and vapor chambers.
• Identify limits in meshing, convergence, model size, solver performance, compute capacity, and simulation time; recommend model reduction, surrogate methods, automation, or additional computing resources.
• Use Python and AI-assisted coding to automate setup, execution, post-processing, optimization, visualization, and reporting. Validate all generated code and models through physics checks, conservation, mesh independence, benchmarks, cross-tool comparison, and test correlation.
• Build simulation DoEs, response surfaces, and Pareto diagrams to balance temperature, thermal resistance, pressure drop, flow uniformity, package limits, manufacturability, robustness, cost, and reliability.
• Convert simulation trade spaces into targeted experimental plans; correlate results, quantify uncertainty, investigate discrepancies, and improve reusable validation methods.
• Translate customer power maps, coolant conditions, allowable temperatures, pressure-drop limits, and package constraints into practical cooling architectures.
Qualifications:
• Bachelor's, Master's, or PhD in Mechanical Engineering, Aerospace Engineering, Physics, Applied Mathematics, Materials Science, or a related discipline.
• 3+ years of industrial simulation or product-development experience.
• Strong ANSYS Fluent capability and practical experience with conjugate heat transfer, internal flow, pressure-drop prediction, meshing, convergence, and model verification.
• Experience with CAD, DoE, optimization, surrogate models, Python automation, and simulation-to-test correlation.
• Ability to distinguish numerical convergence from physical credibility and translate results into manufacturable design recommendations.
• Experience with HPC, AI accelerators, electronics packaging, liquid cooling, or high-power-density systems is highly desirable.
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