Co-Op, Bayesian Methods for Complex Decision Making

Highlights

• Build autonomous discovery agents that decide next experiments for AI factories.
• Test Bayesian experiment-selection strategies against live scientific campaigns.
• Present findings to ML and experimental teams with option to publish.

Ideal candidate

Quantitative Master's or PhD student fascinated by better experimental decisions under tight budgets. Curious, collaborative researcher eager to test Bayesian ideas on live science campaigns with ML and lab teams.

Requirements

• Pursuing Master's or PhD in statistics, ML, CS, operations research, applied math, or quantitative science
• Solid grounding in Bayesian inference and decision making including priors and posterior predictive distributions
• Research experience in Gaussian processes, Bayesian optimization, bandits, RL, or probabilistic modeling
• Strong Python skills with PyTorch and probabilistic libraries such as Pyro, PyMC, or BoTorch
• Comfort working with small, noisy, heterogeneous datasets
• Clear communication with ML and experimental collaborators