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Niche PhD-level CFD and physics-informed ML role reduces applicant density.
Highly specialized CFD and scientific ML expertise limits cross-industry transferability.
Requires PhD/MTech, deep CFD and physics-informed ML expertise, and production ML skills, so highly selective.
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Develop and apply AI/ML methods focused on CFD, multi-physics simulations involving fluid flow, heat transfer, and thermal management.
Create physics-informed neural networks and surrogate/reduced-order models to accelerate simulations while maintaining engineering accuracy.
Collaborate across domains and with external partners to transition AI/ML research into scalable engineering tools and workflows.
PhD or MTech in Mechanical Engineering, Aerospace Engineering, Applied Mathematics, Computer Science, Physics, or related with emphasis on CFD/computational science.
Strong foundation in computational fluid dynamics, numerical PDE methods, turbulence modeling, and heat transfer.
Proficient programming in Python and experience with ML frameworks like PyTorch, TensorFlow, or JAX.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in applying physics-informed neural networks or scientific ML to engineering/scientific computing problems.
Ability to build end-to-end ML workflows including data preparation, training, hyperparameter tuning, and deployment.
Comfortable working in Linux and HPC environments with parallel and GPU-based computing for advanced simulations.