Computational Materials Science Intern: AI-Driven Battery Innovation

Nissan Motor Iberica SA; Santa Clara (CA); Sur place; USD 55 000 - 69 000; Plein temps

Avantages offerts par ce poste

ERGs

Leadership development programs

Résumé du poste

Nissan is seeking a Computational Materials Science Intern to join the Materials Informatics Team in Santa Clara, CA. You will apply computational modeling, AI, and HPC to advance materials for automotive applications and battery tech, under interdisciplinary collaboration.

The role involves electronic structure calculations, ML potential training for large-scale simulations, and building materials databases, with results shared through technical presentations and reports.

Qualifications

• Must be currently pursuing a PhD in Materials Science, Chemistry, Physics, Chemical Engineering, or a closely related field.
• Publications or significant research contributions demonstrate research excellence.
• Experience with inorganic/organic chemistry, reaction kinetics, surface reactions.

Responsabilités

• Perform electronic structure calculations to study material behaviors.
• Train and fine-tune ML interatomic potentials for MD simulations.
• Build and maintain materials databases with open-source datasets.
• Conduct descriptive analytics to identify trends in large materials datasets.
• Communicate findings via presentations, reports, and publications.

Connaissances

Python programming

Machine learning

TensorFlow

NumPy

SciPy

pandas

Formation

PhD candidate in Materials Science, Chemistry, Physics, Chemical Engineering, or related field

Outils

VASP

Gaussian

MATLAB

Description du poste

Nissan is seeking a Computational Materials Science Intern to join the Materials Informatics Team in Santa Clara, CA. You will apply computational modeling, AI, and HPC to advance materials for automotive applications and battery tech, under interdisciplinary collaboration.

The role involves electronic structure calculations, ML potential training for large-scale simulations, and building materials databases, with results shared through technical presentations and reports.