Co-Op, Bayesian Methods for Complex Decision Making

What you'll do

• Design and benchmark experiment-selection strategies under realistic budgets.
• Build agents that propose experiments using surrogate models and develop Bayesian-inspired in-context learning approaches.
• Evaluate methods on campaign data, report findings to ML and experimental teams, and potentially contribute to a publication.

What they ask for

• Pursuing a Master's or PhD in statistics, ML, CS, operations research, applied math, or a quantitative science.
• Grounding in Bayesian inference and decision making, including priors and posterior predictive distributions.
• Research experience in Gaussian processes, Bayesian optimization, bandits, reinforcement learning, or probabilistic modeling.
• Strong Python skills with PyTorch and probabilistic libraries such as Pyro, PyMC, or BoTorch.

Nice to have

• Experience applying these methods to materials science, chemistry, or another physical science.
• Familiarity with in-context learning as implicit Bayesian inference, meta-learning, prior-data fitted networks, or optimization in categorical or combinatorial spaces.

Tools

Python, PyTorch, Pyro, PyMC, BoTorch

AI focus

Develop Bayesian methods, in-context learning approaches, and autonomous discovery agents to select experiments and accelerate scientific discovery.