Co-op,LLMsforDecisionMaking

Your Impact at LILA Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Within Physical Sciences AI, our decision making efforts develop the algorithms that drive experimental decision-making, closing the loop between models, experiments, and the next thing to try. We're now exploring how large language models can extend that capability: encoding domain priors, proposing candidates, reasoning over campaign history, and pairing naturally with established algorithms like Bayesian optimization for sample-efficient search. As an LLMs for Decision Making Co-Op, you will work at the intersection of LLMs and Bayesian optimization, prototyping and evaluating approaches that combine language model reasoning with principled experimental design. Your work will land in the decision making stack that powers experimental campaigns across Lila's AI Science Facilities. What You'll Be Building Contribute to LLM-based decision-making methods for experimental campaigns, focused on a well-defined sub-problem Prototype approaches that combine LLM reasoning with Bayesian optimization, active learning, or design of experiments, with mentor guidance Build evaluation frameworks that benchmark LLM-augmented strategies against established Bayesian baselines Help integrate promising methods into the decision making stack used across physical sciences campaigns Document findings and share results through write-ups, presentations, or contributions to internal libraries What You'll Need to Succeed Pursuing a Master's or PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, Physics, Chemistry, Materials Science, or a related quantitative field Strong programming skills in Python and familiarity with ML frameworks such as PyTorch, JAX, or similar Foundation in Bayesian methods, Bayesian optimization, or probabilistic modeling Experience with large language models including fine-tuning, test-time compute, and benchmarking in applied