AI Residency Program, Material Science (2026 Cohort)

Lila Sciences Lila Sciences · AI Frontier · One Charles Park, Cambridge, MA · Physical Sciences AI

Research residency program focused on applying AI/ML to materials science, exploring areas like ML-accelerated simulations, generative models, and agentic science. The role involves designing and executing independent research projects, collaborating with scientists, and contributing to open-science initiatives. While publishing is encouraged, the core focus is on advancing scientific discovery.

What you'd actually do

  1. Design and execute independent research projects in AI for materials science
  2. Collaborate with Lila scientists and engineers on cutting-edge, open-science initiatives
  3. Explore domains such as ML-accelerated simulations, Bayesian methods, representation learning, generative AI, agentic science, and ML-driven automation
  4. Contribute to collaborative team research and co-develop novel approaches to scientific discovery
  5. Share findings internally and externally; publications are welcome but not mandatory

Skills

Required

  • Degree in Materials Science, Chemistry, Computer Science, AI/ML, Physics, Mathematics, or related field (Bachelor’s, Master’s, or PhD)
  • Proficiency in Python and deep learning frameworks (e.g., PyTorch)
  • Experience working with large-scale datasets or simulations
  • Familiarity with modern AI/ML architectures and training techniques
  • Strong research background, demonstrated through publications, thesis work, or open-source projects

Nice to have

  • Prior work on ML applications in scientific domains (e.g., materials discovery, chemistry, simulations)
  • Familiarity with Bayesian optimization, active learning, or generative models
  • Experience in reinforcement learning or agent-based approaches to scientific reasoning
  • Open-source contributions or collaborative research experience
  • Strong communication and writing skills, especially for conveying complex scientific ideas

What the JD emphasized

  • research proposal (up to 3 pages, unlimited references)
  • Applications without both documents will not be considered

Other signals

  • AI for materials science
  • ML-accelerated simulations
  • representation learning
  • generative models
  • agentic science
  • ML-driven automation
  • pushing the frontier of scientific discovery