New College Grad - Computational Physics & Agentic AI Engineer, Doctorate Degree (Spring 2027)
About The Position
Applied Materials is hiring an early-career PhD-level scientist or engineer to join a computational modeling team developing and applying physics-based simulation tools and digital twins for semiconductor process chambers and plasma processing applications. The role focuses on improving understanding and prediction of thermal and plasma-driven processes—including deposition, etching, plasma-enhanced processing, and plasma-surface interactions—and making modeling capabilities more accessible to engineers through AI-driven software and workflows. The position combines first-principles modeling, large-scale simulation, software development, computational physics, high-performance computing, and AI/ML. The engineer will develop multi-scale and multi-fidelity models, connect high-fidelity solvers with reduced-order and surrogate approaches, and establish methods to verify computational models and validate predictions against reference solutions and experimental data. Applications are reviewed on a rolling basis; candidates are asked to apply by November 20, 2026, though the position may close earlier.
Requirements
- MS or PhD in Mechanical Engineering, Electrical Engineering, Aerospace Engineering, Chemical Engineering, Materials Science, Physics, or a related field.
- Demonstrated experience in computational physics or multiphysics modeling, including one or more areas such as transport phenomena, fluid dynamics, heat transfer, chemistry, thermal-structural mechanics, plasma dynamics, or feature-scale process modeling.
- Strong background in applied mathematics and computational methods, including numerical methods, statistical modeling, uncertainty quantification, or computational solver development.
- Strong programming experience in Python and/or C++, with experience developing scientific or engineering applications.
- Experience with machine learning for scientific or engineering applications, such as surrogate modeling, physics-informed AI, neural operators, or related data-driven approaches.
- Experience working with simulation data and computational models to analyze complex physical or engineering systems.
- Strong technical judgment and attention to detail.
- Excellent written and verbal communication skills.
Nice To Haves
- PhD degree preferred.
- Experience with high-performance computing, parallel computing, or GPU acceleration for scientific and engineering applications.
- Experience with LLM-based agents, tool use, or agentic workflow orchestration for technical or scientific applications.
- Experience with multi-fidelity modeling, reduced-order modeling, surrogate modeling, or methods connecting models across different levels of fidelity.
- Familiarity with commercial, open-source, or in-house multiphysics simulation platforms such as ACE+, COMSOL, ANSYS Fluent, OpenFOAM, or similar tools.
- Experience developing scientific software, reusable modeling frameworks, engineering workflow automation, or digital twins.
- Experience applying computational modeling to semiconductor process or equipment applications, such as thermal processing, plasma processing, deposition, etching, or related multiphysics problems.
Responsibilities
- Apply computational modeling to semiconductor process and equipment development challenges, including thermal, plasma, deposition, etch, and related multiphysics applications.
- Develop, implement, and improve physics-based simulation capabilities, numerical methods, and engineering workflows.
- Use simulation, experimental, and engineering data to analyze process and hardware behavior, identify physical mechanisms, and support design decisions.
- Develop and evaluate reduced-order, surrogate, and machine-learning models to improve computational efficiency or predictive capability.
- Build AI-enabled tools and agentic workflows that help engineers set up, execute, analyze, and interpret computational models.
- Collaborate with process, hardware, experimental, and software teams to define modeling problems, interpret results, and translate insights into engineering decisions.
- Verify and validate modeling and AI-enabled capabilities using reference solutions, simulation benchmarks, and experimental data.
- Contribute to internal modeling software, reusable workflows, documentation, and best practices.
- Communicate modeling results, assumptions, limitations, and recommendations to scientists, engineers, technical leadership, and customers.
Benefits
- Comprehensive benefits package
- Potential eligibility for a bonus program
- Potential eligibility for a stock award program
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
