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Tier-1 brand and Bangalore location but niche scientific-ML specialization reduces candidate pool.
Requires physics/semiconductor domain knowledge and scientific ML, limiting transferability across industries.
Prefers PhD plus specialized scientific-ML skills, HPC and distributed training experience.
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Develop advanced AI models integrating physics and scientific computing for semiconductor engineering applications.
Design and train surrogate, operator-learning, and foundation models to accelerate simulations and engineering designs.
Build scalable workflows for data generation, model training, validation, and deployment in scientific AI contexts.
Ph.D. in Mechanical Engineering, Chemical Engineering, Materials Science, Physics, Applied Mathematics, Computer Science, or related fields.
Strong expertise in scientific machine learning and numerical simulation techniques.
Proficiency in Python and PyTorch; experience with HPC and distributed training is expected.
Work Experience Required: Not explicitly mentioned in the JD.
Expertise in surrogate modeling, physics-informed neural networks (PINNs), operator learning such as Fourier Neural Operators (FNO), or foundation models.
Experience working at the intersection of physics-based simulation and machine learning to solve engineering problems.
Comfortable collaborating with domain experts and driving technical innovation in AI for scientific and semiconductor engineering applications.