Co-Op, AI-Driven Equation Discovery

Your Impact at LILA

The PS AI team sits at the intersection of Physical Sciences R&D and AI/ML. Lila runs high-throughput experiments across electrochemistry, formulations, and advanced materials, generating rich datasets that demand more than black-box models.

As a Co-Op focused on AI-driven equation discovery, you will develop and benchmark methods that extract interpretable governing equations directly from that data. Discovered equations reveal the underlying physics, make predictions explainable, and serve as mechanistic building blocks for digital twins and closed-loop optimization.

You will work alongside PS AI researchers and physical scientists, helping shape how Lila turns experimental data into scientific understanding at scale.

What You'll Be Building

• Build end-to-end equation discovery pipelines from preprocessing through candidate generation and validation
• Benchmark symbolic regression methods (SINDy, PDE-FIND, PySR, LLM-guided search) on Lila campaign data
• Apply equation discovery to live electrochemistry, formulation, and materials datasets
• Design evaluation harnesses measuring recovery rate, generalization, noise robustness, and physical consistency
• Embed discovered equations into surrogate models, physics-informed models, and experimental digital twins
• Translate open scientific questions into concrete problems with datasets, baselines, and success criteria
• Document and present findings across PS AI and domain science teams

What You'll Need to Succeed

• 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

Bonus Points For

• Working knowledge of symbolic or sparse regression methods (SINDy, PySR, PDE-FIND, or equivalent)
• Experience with LLM-guided or agentic symbolic regression (LLM-SR, SR-Scientist, or similar)
• Background in physics-informed ML, uncertainty quantification, or operator learning (PINNs, neural ODEs, FNO, DeepONet)
• Exposure to digital twin or closed-loop workflows: model calibration, active learning, Bayesian optimization
• Domain knowledge in electrochemistry, formulations, superconductors, or materials science