2026 Summer Physicist/Scientist Intern - PhD (Santa Clara, CA)
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About this role
2026 Summer Intern PhD Physicist/Scientist Computational Modeling/ML
Applied Materials is the leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. Our expertise in modifying materials at atomic levels and on an industrial scale helps our customers – who make smartphones, supercomputers, virtual reality headsets, autonomous vehicles and more – transform their ideas into reality. Our innovations Make Possible® a Better Future.
Applied Materials’ Computational Products and Solutions (CPS) group is seeking a highly motivated intern to join our team in Summer 2026. Our team develops state‑of‑the‑art multi‑physics models of Applied Materials process chambers and collaborates closely with internal product development teams to optimize designs for next‑generation semiconductor applications. We also develop and maintain ACE+, our commercial multi‑physics simulation platform used by leading technology companies worldwide.
As an intern in the Computational Products & Solutions group, you will contribute to the development and optimization of simulations and modeling tools supporting next‑generation semiconductor applications. The position will entail the following:
- Multiphysics simulation and modeling of Applied Materials chambers
- Workflow automation
- Develop pipelines for training machine learning surrogate models
- Perform testing and optimization of modeling software modules
Requirements
- Student must be pursuing a PhD degree program in Aerospace, Mechanical, Chemical, Electrical Engineering or a related field
- Student must be in good academic standing at their university, with a preferred GPA of 3.0 or above on a 4.0 scale
- Programming experience (C/C++/Matlab/Python/Fortran etc)
- Experience with physics simulation software for fluids, plasma, or electromagnetic applications
- Ability to rapidly understand new physics and simulation frameworks
- Experience with plasma semiconductor processing technology preferred
- Experience with machine learning models and large data sets preferred