Computational Material Science Intern

What you'll do

• Perform electronic structure calculations to investigate fundamental mechanisms behind experimentally observed material behaviors.
• Train and fine-tune machine learning interatomic potentials for large-scale molecular dynamics simulations.
• Build and maintain materials databases using open-source datasets and high-throughput computational results.
• Conduct descriptive analytics to identify trends and opportunities within large materials datasets.
• Analyze and communicate research findings through technical presentations, reports, and publications.
• Automate high-throughput calculations on high-performance computing (HPC) systems.

What we're looking for

• Must be currently pursuing a PhD in Materials Science, Chemistry, Physics, Chemical Engineering, or a closely related field.
• Must demonstrate research excellence through publications, conference presentations, patents, or other significant research contributions.
• Must have an advanced understanding of inorganic and/or organic chemistry, reaction kinetics, and surface reactions.
• Must have experience performing electronic structure calculations using tools such as VASP, Gaussian, or similar platforms.
• Must have experience automating high-throughput calculations on high-performance computing (HPC) systems.
• Must have experience with machine learning, statistical modeling, and advanced Python programming using TensorFlow, NumPy, SciPy, and pandas.
• Must be legally authorized to work in the United States.
• Experience with generative AI models for battery discovery, electrochemistry, and molecular dynamics simulations (preferred).