Machine Learning for Digital Twins Co-Op

Responsibilities

• Contribute to ML models for scientific and experimental systems, focused on a well-defined digital twin sub-problem
• Build and train surrogate, operator-learning, or physics-informed models against experimental and simulation data
• Calibrate models, quantify uncertainty, and validate against data from active AISF experimental campaigns
• Frame open-ended scientific questions as concrete ML tasks with clear datasets, baselines, and evaluation criteria
• Document findings and share results through write-ups and presentations

Requirements

• Pursuing a Master's or PhD in Machine Learning, Computer Science, Applied Mathematics, Physics, or a related quantitative field
• Strong programming skills in Python
• Hands-on experience with ML frameworks such as PyTorch, JAX, or TensorFlow
• Experience applying machine learning to scientific, engineering, physical, or experimental systems
• Familiarity with neural operators, spatiotemporal modeling, or physics-informed ML
• Solid foundation in model training, validation, debugging, and experiment tracking
• Ability to work with messy or heterogeneous scientific datasets

Preferred Qualifications

• Experience with Fourier Neural Operators, DeepONets, or graph neural operators
• Experience with digital twins, model calibration, and uncertainty quantification
• Experience with closed-loop scientific decision-making, active learning, or Bayesian optimization
• Knowledge of applications in materials science, chemistry, energy systems, or fluid dynamics

About the Company

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously.