Co-Op, AI-Driven Equation Discovery

Highlights

• Build end-to-end pipelines that discover interpretable equations from lab data.
• Turn electrochemistry and materials data into physics for digital twins.
• Help shape how experimental data becomes scientific understanding at scale.

Ideal candidate

A curious Master's or PhD researcher who loves messy scientific data and wants interpretable, physics-grounded AI to power discovery and digital twins.

Requirements

• Pursuing a Master's or PhD in ML, CS, applied math, physics, materials science, or chemical engineering
• Strong Python skills with hands-on experience in PyTorch, JAX, or similar
• Experience applying machine learning to messy, real-world scientific or experimental data
• Solid grounding in linear algebra, calculus, and probability
• Comfort reading and implementing methods from ML research papers
• Ability to frame open-ended scientific questions as concrete ML tasks
• Clear communication across research, engineering, and physical science teams