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Niche physics-ML CFD specialization reduces candidate pool despite metro location.
Highly domain-specific CFD and physics-ML requirements limit cross-industry portability.
PhD requirement plus mandatory CFD and physics-informed ML expertise enforce high shortlisting strictness.
Develop and apply AI/ML methods specifically targeting CFD and multi-physics simulations in fluid flow, heat transfer, and thermal management.
Build and deploy physics-informed neural networks, surrogate, and reduced-order models to accelerate simulation workflows while maintaining engineering fidelity.
Collaborate with research, product teams, and external partners to translate advanced physics-based AI methods into scalable, production-ready engineering tools.
PhD or MTech in Mechanical Engineering, Aerospace Engineering, Applied Mathematics, Computer Science, Physics, or related field with strong CFD/computational science emphasis.
Demonstrated experience applying physics-informed neural networks or similar scientific ML methods to engineering or scientific computing problems.
Strong programming skills in Python and experience with ML frameworks like PyTorch, TensorFlow, or JAX.
Work Experience Required: Not explicitly mentioned in the JD.
Expertise combining computational fluid dynamics, turbulence modeling, numerical PDE methods, and scientific ML targeting engineering simulation acceleration.
Experience working with Linux/HPC environments including parallel/GPU computing for ML workloads.
History of engaging in multi-disciplinary collaboration bridging research, engineering, and product teams to deploy physics-based AI solutions in commercial or research settings.