Co-Op, AI-Driven Equation Discovery

Preferred

• 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
• Lila Sciences is building Scientific SuperintelligenceTM to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
• LILA combines advanced AI models with proprietary AI Science FactoryTM instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
• Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.