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Niche PhD-level CFD plus scientific-ML specialization reduces candidate pool despite metro location.
Role requires domain-specific CFD and physics-ML expertise, limiting cross-industry transferability.
PhD requirement and specialized CFD/scientific-ML skills make shortlisting highly selective.
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Develop and apply advanced AI/ML methods specifically for CFD and multi-physics simulation focusing on fluid flow, heat transfer, and thermal management.
Build and deploy physics-informed neural networks, surrogate and reduced-order models to accelerate and improve fidelity of engineering simulations and workflows.
Collaborate with research, product teams, and external partners to translate scientific ML research into scalable, production engineering tools and workflows.
PhD or MTech in Mechanical Engineering, Aerospace Engineering, Applied Mathematics, Computer Science, Physics, or closely related field with emphasis on CFD or computational science.
Strong expertise in computational fluid dynamics, numerical methods for PDEs, turbulence modeling, and heat transfer.
Proven experience with physics-informed neural networks or related scientific ML methods applied to engineering problems.
Strong programming skills in Python and familiarity with scientific ML frameworks (PyTorch, TensorFlow, or JAX). Work Experience Required: Not explicitly mentioned in the JD.
Expertise combining computational fluid dynamics, scientific computing, and modern ML to accelerate simulation and design optimization in engineering contexts.
Experienced in building end-to-end ML workflows in HPC/Linux environments with understanding of model verification and uncertainty quantification.
Capable of bridging research and applied product teams to operationalize physics-based AI technologies for complex thermal-fluid systems.