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New College Grad - Computational Physics & Agentic AI Engineer, Doctorate Degree (Spring 2027)

Applied Materials Santa Clara, CA

Santa Clara, CA

Today

$138k - $190k/yr Full time Agentic Ai Ai Agent Artificial Intelligence

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Job Description

Applied Materials is seeking a New College Grad computational physics/plasma modeling and agentic AI engineer to help build physics-based simulation tools and digital-twin workflows for semiconductor process chambers. The role combines multiphysics modeling, verification and validation, and AI-enabled automation to improve both predictive performance and usability for engineering teams.

Role Focus

This position centers on three core areas of work:

Modeling Capability — develop and apply advanced multi-scale and multi-fidelity models for thermal and plasma-based semiconductor processes, connecting high-fidelity solvers with reduced-order and surrogate approaches, with quantified accuracy and defined limits of validity.
Verification & Validation — establish rigorous methods to verify computational models and AI-enabled workflows against reference solutions and validate predictions against experimental data, including the infrastructure required to maintain reliability as capabilities evolve.

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 key physical mechanisms, and support design decisions.
Develop and evaluate reduced-order, surrogate, and machine-learning models when they 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 modeling insights into engineering decisions.
Verify and validate modeling and AI-enabled capabilities using appropriate reference solutions, simulation benchmarks, and experimental data.
Contribute to the development and maintenance of internal modeling software, reusable workflows, documentation, and best practices.
Communicate modeling results, assumptions, limitations, and recommendations clearly to scientists, engineers, technical leadership, and customers.

Required Qualifications

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++, including experience developing scientific or engineering applications.
Experience with machine learning for scientific or engineering applications, including 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.

Technologies

Python, C++, machine learning
Surrogate modeling, physics-informed AI, neural operators
LLM-based agents
High-performance computing, parallel computing, GPU acceleration
ACE+, COMSOL, ANSYS Fluent, OpenFOAM

Position Details

Location: Santa Clara, CA (onsite)
Time Type: Full time
Employee Type: New College Grad
Salary: USD 138,000 - 190,000 per yearly
Travel: Yes, 10% of the Time
Relocation Eligible: Yes
Education: MS or PhD

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