Physics AI Scientist

Applied Materials
Bengaluru
Workplace: OnsiteFull timeFunction: Research & Scientific (R&D)Education: phdSkills: ["Collaboration","Communication","Innovation"]

Develop next-generation Scientific AI models for semiconductor engineering by combining physics and chemistry domain knowledge with modern machine learning. Build physics-aware surrogate and foundation models, operator-learning approaches, and end-to-end data generation, training, validation, and deployment workflows for scientific simulations. Collaborate with domain experts to solve complex engineering challenges, publish technical innovations, and drive adoption of Scientific AI across engineering applications.

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FursaFursa
Applied Materials
Applied Materials
6 days ago

Physics AI Scientist

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Source: Company careers pageValidated by: Fursa AI
Last checked: 9 hours agoStatus: Live

Job Summary

Develop next-generation Scientific AI models for semiconductor engineering by combining physics and chemistry domain knowledge with modern machine learning. Build physics-aware surrogate and foundation models, operator-learning approaches, and end-to-end data generation, training, validation, and deployment workflows for scientific simulations. Collaborate with domain experts to solve complex engineering challenges, publish technical innovations, and drive adoption of Scientific AI across engineering applications.
Location: Bengaluru
Workplace: Onsite
Employment Type: Full time
Job Function: Research & Scientific (R&D)

Key Responsibilities

  • •Develop Scientific AI models bridging 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.
Travel: Low travel

Key Requirements

  • •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.
Experience:Scientific machine learningNumerical simulationHPCDistributed trainingScientific datasets
Education:PhD / Doctorate in Mechanical Engineering, Chemical Engineering, Materials Science, Physics, Applied Mathematics, Computer Science (or related field)
Skills:CollaborationCommunicationInnovation
Tech Stack:PythonPyTorchPINNsOperator learningFNOHPCDistributed trainingSurrogate modelingFoundation models

Company Brief

Applied Materials
Provides semiconductor manufacturing equipment, services, and software used to build advanced chips and display technologies. Its tools support materials engineering, deposition, etching, inspection, and process control for major electronics manufacturers worldwide.
Industry: Industrial Machinery
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Santa Clara, United States
Founded: 1967
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Glassdoor: 3.9
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