Co-Op, Bayesian Methods for Complex Decision Making at Lila Sciences · LumiJobs

Qualification

Comfort working with smallGuided by our core values of truth

Pursuing a Master's or PhD in statistics, ML, CS, operations research, applied math, or a quantitative science
Solid 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
Comfort working with small, noisy, and heterogeneous datasets
Clear communication with both ML and experimental collaborators

Applying these methods to real problems in materials science, chemistry, or another physical science
Familiarity with in-context learning as implicit Bayesian inference, meta-learning, or prior-data fitted networks
Optimization over categorical or combinatorial spaces, including mixed-variable, multi-objective, or batch settings
Reinforcement learning for sequential decision making or Bayesian experimental design
Training models on multimodal scientific data such as text, images, spectra, or simulations
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.

Co-Op, Bayesian Methods for Complex Decision Making