below marketmost similar roles pay hereabove market
This role pays less than
52%
of similar roles. Most pay
$133,375–$231,150
— the blue band above.
At the midpoint, this role pays about
$164k
versus about
$182k
for comparable roles.
Based on 240 similar postings.
Employer
About Applied Materials
Applied Materials is the world''s largest supplier of equipment, services, and software for the semiconductor and display industries, enabling the production of chips and advanced displays. Industry: Semiconductor Equipment
Applied Materials currently has
363 open roles
on FindRole.
Listed pay typically runs
$124,000–$171,000
across 229 roles with salary data.
JOB TITLE: New College Grad - Computational Physics & Agentic AI Engineer, Doctorate Degree (Spring 2027)
As a New College Grad - Computational Physics & Agentic AI Engineer, Doctorate Degree (Spring 2027), you will join the computational modeling team to develop physics-based simulation tools and digital twins for semiconductor process chambers. You will build multi-scale and multi-fidelity models for thermal and plasma-driven processes, including deposition, etching, and plasma-surface interactions. Your daily work involves developing numerical methods, reduced-order models, and AI-enabled agentic workflows to improve predictive capabilities. You will utilize Python, C++, and machine learning techniques like physics-informed AI and neural operators. The role requires applying computational physics and high-performance computing to solve complex multiphysics problems in semiconductor equipment, ensuring model reliability through rigorous verification against experimental data and simulation benchmarks to accelerate technology and product development.
Develop and apply multi-scale, multi-fidelity models for thermal and plasma-based semiconductor processes.
Implement physics-based simulation capabilities, numerical methods, and engineering workflows for deposition and etching.
Analyze process and hardware behavior using simulation, experimental, and engineering data to support design decisions.
Create reduced-order, surrogate, and machine-learning models to improve computational efficiency and predictive accuracy.
Build AI-enabled tools and agentic workflows to help engineers execute and interpret computational models.
Verify and validate modeling and AI-enabled capabilities against reference solutions and experimental data.
Maintain internal modeling software, reusable workflows, documentation, and technical best practices.
Communicate modeling results, assumptions, and technical recommendations to scientists, engineers, and customers.
What we're looking for
MS or PhD in Mechanical, Electrical, Aerospace, Chemical Engineering, Materials Science, Physics, or a related field.
Demonstrated experience in computational physics or multiphysics modeling, including transport phenomena, fluid dynamics, heat transfer, chemistry, or plasma dynamics.
Strong background in applied mathematics and computational methods, including numerical methods, statistical modeling, and uncertainty quantification.
Strong programming experience in Python and/or C++ for developing scientific or engineering applications.
Experience with machine learning for scientific or engineering applications, such as surrogate modeling or physics-informed AI.
Experience working with simulation data and computational models to analyze complex physical or engineering systems.
Experience with high-performance computing, parallel computing, or GPU acceleration for scientific and engineering applications (preferred).
C++
Python
High-Performance Computing
GPU Programming
Numerical Simulation
Monte Carlo
Finite Element Method
Multiphysics Modeling
Machine Learning
Digital Twin
Parallel Algorithms
Computational Geometry
Level-set Methods
Ray Tracing
Ballistic Transport Models
Kinetic Monte Carlo
Python
PyTorch
TensorFlow
JAX
GPyTorch
BoTorch
machine-learning
Surrogate Modeling
Physics-Informed Neural Networks
Design of Experiments
Monte Carlo
SEM
TEM
Uncertainty Quantification
Bayesian Calibration
Reaction Kinetics
Transport Phenomena
Generative AI
PyTorch
Reinforcement Learning
Video Generative Models
Video-Language Models
Physics Simulation
3D Generative Models
Distributed Training
Data Processing Pipelines
Foundation Models
Robotics
Open-source
Python
C++
PyTorch
CUDA
Reinforcement Learning
Deep Learning
Robotics
Autonomous Vehicles
Physical AI
Parallel Programming
Simulation
Closed-loop Training
Verification and Validation
Embodied AI