Physicist/Scientist, Doctorate Degree, New College Grad (Spring 2027)
About The Position
Applied Materials is seeking a highly motivated PhD-level scientist or engineer to develop and apply advanced modeling capabilities for plasma processing applications, including plasma etching, plasma-enhanced deposition, and atomic layer etching (ALE). The role focuses on understanding, modeling, and predicting plasma–surface interaction processes using physics-based methods and data-driven approaches. The successful candidate will use atomistic and mesoscale modeling techniques such as ab initio quantum chemistry, molecular dynamics (MD), and kinetic Monte Carlo (kMC). They will also develop and deploy machine learning models trained on data from large-scale, multidimensional high-performance computing (HPC) simulations, complemented by experimental measurements. The position brings together first-principles modeling, large-scale simulation, and AI/ML methods to support accelerated technology and product development in the semiconductor equipment industry. The role is based in Santa Clara, California. It is a full-time, new-college-graduate position and involves travel 10% of the time. Relocation is eligible. Applications are reviewed on a rolling basis; applicants are asked to apply by November 20, 2026, though the position may close earlier depending on application volume or candidate selection.
Requirements
- PhD in Engineering (such as Chemical, Materials, or Mechanical Engineering), Science (such as Physics or Chemistry), or a related field.
- Demonstrated experience modeling plasma–surface or gas–surface interaction processes using ab initio quantum chemistry, molecular dynamics, and/or kinetic Monte Carlo methods.
- Strong background in machine learning, particularly in scientific computing or HPC environments.
- Experience working with large, complex simulation datasets and integrating modeling results with experimental data.
Responsibilities
- Develop and apply advanced models for plasma etching, deposition, and plasma-based surface modification processes.
- Combine physics-based and machine-learning-driven approaches to improve process understanding and predictive capability.
- Collaborate with experimental teams to guide process development and interpret experimental results.
- Contribute to the development, validation, and maintenance of internal software tools, modeling frameworks, and best practices.
- Communicate modeling results clearly and effectively to multidisciplinary teams, including scientists, engineers, and major customers.
Benefits
- Comprehensive benefits package.
- Candidates may be eligible for participation in a bonus program and a stock award program, as applicable.
- Relocation eligible.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
