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Highly specialized physics-ML role lowers applicant density despite remote/metro visibility.
Requires deep CFD and physics-ML expertise, limiting transferability across unrelated industries.
PhD requirement plus niche CFD and scientific ML expertise enforce strict shortlisting filters.
Develop and apply advanced AI/ML methods specifically for computational fluid dynamics (CFD) and multi-physics simulation problems in fluid flow, heat transfer, turbulence, and thermal management.
Create and deploy physics-informed neural networks, surrogate models, and reduced-order models to accelerate simulations while maintaining engineering accuracy.
Collaborate with research, product teams, and external vendors to translate scientific ML research into production-ready tools and scalable engineering workflows.
PhD or MTech in Mechanical Engineering, Aerospace Engineering, Applied Mathematics, Computer Science, Physics, or related fields with strong emphasis on CFD or computational science.
Strong foundation in computational fluid dynamics, numerical methods for PDEs, turbulence modeling, and heat transfer.
Demonstrated experience with physics-informed neural networks or related scientific ML methods applied to engineering/scientific problems.
Strong programming skills in Python and experience with ML frameworks such as PyTorch, TensorFlow, or JAX.
Experienced in building end-to-end ML workflows including data preparation, training, evaluation, and deployment in HPC/Linux environments with GPU-based training.
Skilled at integrating AI/ML models with CFD solvers, design optimization, digital twin platforms, and simulation automation workflows.
Capable of driving technical strategy in physics AI, scientific machine learning, model validation, and complex engineering optimization based on strong interdisciplinary knowledge.