About this role
Who We Are
Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the worlds technology.
What We Offer
Location:Bangalore,IND At Applied Materials, we care about the health and wellbeing of our employees. Were committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our . Role Summary Develop next-generation AI models for semiconductor engineering by combining scientific domain knowledge with modern machine learning. Build physics-aware surrogate models, foundation models, and optimization workflows that accelerate simulation and engineering design. Key Responsibilities
Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the worlds technology.
What We Offer
Location:Bangalore,IND At Applied Materials, we care about the health and wellbeing of our employees. Were committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our . Role Summary Develop next-generation AI models for semiconductor engineering by combining scientific domain knowledge with modern machine learning. Build physics-aware surrogate models, foundation models, and optimization workflows that accelerate simulation and engineering design. Key Responsibilities
- Develop Scientific AI models that bridge scientific computing, physics-based simulation and machine learning for physics and chemistry-based engineering problems.
- Design and train surrogate models, operator-learning models, and foundation models for scientific simulations.
- Collaborate with domain experts to formulate AI solutions for complex engineering challenges.
- Develop scalable data generation, training, validation and deployment workflows for Scientific AI models.
- Publish technical innovations and drive adoption of Scientific AI across engineering applications.
Preferred Qualifications
- Ph.D. in Mechanical Engineering, Chemical Engineering, Materials Science, Physics, Applied Mathematics, Computer Science, or a related field.
- Strong background in scientific machine learning and numerical simulation.
- Experience with surrogate modeling, PINNs, operator learning (e.g., FNO), or foundation models.
- Proficiency in Python and PyTorch.
- Experience with HPC, distributed training, large-scale scientific datasets or scalable ML workflows.
Skills
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