Applied Materials Fall - Physicist/Scientist Co-Op 

United States, California 

924598607

Santa Clara,CA

Key Responsibilities:

• Chemistry and materials design for screening and optimization applications with special emphasis on at least one of the following areas: thermal transport, polymer materials formation and viscosity, low and high dielectric materials, work function metals, precursor design for purity of deposition, and selectivity in etch chemistry
• Scope the diverse cutting-edge materials and chemistry discovery tools on the market for potential future value and prioritize what methods warrant further exploration
• Evaluate a variety of MLIP’s for validation required property prediction and potential extend to fine-tuning of MLIP’s for increased accuracy.
• Recommend atomic-scale changes to structure and/or processing conditions to influence observed properties
• Provide parameters, inputs, and/or guidance for higher-level process, feature, and reactor models
• Hypothesize and test mechanisms to determine the root-cause of observed behavior
• Work effectively with external vendor software providers in testing new features and functionality of new products
• Work effectively with internal multi-disciplinary teams of experimentalists, modelers, and computing infrastructure support teams
• Demonstrate analytical and engineering judgement to scope and assess the appropriate level of model required
• Proactively validate/correlate results with real world data to improve models
• Present complex in-depth technical material in a concise manner
• Advocate for the power of modeling while also communicating limitations
• Serve as a business unit chemistry, materials, or physics expert

Minimum Qualifications:

• Must have a PhD or Master’s degree in Material Science, Chemical Engineering, Mathematics, Physics, or a related field with an availability range between August 2025 – Dec 2025
• Experience or academics with:
  • FirstPrinciples/Ab-initio/DensityFunctional Theory (local orbital and plane wave), Molecular Dynamics, and/or Monte Carlo methods
  • Machine learning interatomic potentials both at the training stage and inference stage. Experience with both universal and focused approaches a plus.
  • Programming (Python, C, Fortran, etc.), scripting (python, Linux), and databases
• Knowledge of informatics, artificial intelligence, machine learning, and data science approaches

Preferred Qualifications:

• Polymer structure creation to property prediction such as viscosity
• Inverse design
• Enumerative atomic structure model prediction (diffusion models and beyond)
• Multi-step reaction network discovery
• Materials and/or chemistry discovery

Full time

Intern / Student