First principles density functional theory experience
Machine learning interatomic potentials training
Polymer modeling workflows and property prediction
Applied Materials is a global leader designing equipment for the semiconductor and display industries to enable technologies like AI and IoT
Job Summary
Applied Materials is a global leader designing equipment for the semiconductor and display industries to enable technologies like AI and IoT.
The role involves recommending atomic-scale changes to structure and processing conditions to influence observed material properties using advanced modeling.
Candidates will work within a supportive culture that encourages learning, professional growth, and pushing the boundaries of materials science innovation.
Matching Summary
Applied Materials is a global leader designing equipment for the semiconductor and display industries to enable technologies like AI and IoT.
Salary
Not specified; Not specified; Not specified
Skills & Requirements
Must-have
First Principles Density Functional Theory experience
Machine Learning Interatomic Potentials training
Polymer modeling workflows and property prediction
Python scripting and Linux environment proficiency
Molecular Dynamics and Monte Carlo simulation methods
Nice-to-have
Experience with universal MLIP approaches
Knowledge of LLM and GNN architectures
Ability to communicate technical limitations clearly
Collaboration with external vendor software providers
Background in non-carbon based polymer chemistry
Key Requirements
Extensive experience with Ab-initio and DFT methods
Proven track record in Machine Learning Interatomic Potentials
Proficiency in Python, C, Fortran, and SQL databases