Co-op: Build Interpretable Physics Equations from Data

Lila Sciences; Cambridge (MA); On-site; USD 34,000 - 48,000; Part time

Job summary

Lila Sciences is seeking a Co-Op focused on AI-driven equation discovery to turn experimental data into interpretable governing equations. You will develop and benchmark methods that reveal the underlying physics, enable digital twins, and support closed-loop optimization.

You will collaborate with PS AI researchers and physical scientists, building end-to-end pipelines from preprocessing to validation and applying methods to electrochemistry, formulations, and materials datasets.

Qualifications

• 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.

Responsibilities

• 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 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.

Skills

Python

PyTorch

JAX

ML experience

Education

Master's or PhD in ML/CS

Applied math/Physics/Materials science/Chemical engineering

Tools

PyTorch

JAX

Job description

Lila Sciences is seeking a Co-Op focused on AI-driven equation discovery to turn experimental data into interpretable governing equations. You will develop and benchmark methods that reveal the underlying physics, enable digital twins, and support closed-loop optimization.

You will collaborate with PS AI researchers and physical scientists, building end-to-end pipelines from preprocessing to validation and applying methods to electrochemistry, formulations, and materials datasets.