Co-op, Machine Learning for Digital Twins
Core
Building and training ML models (surrogate, operator-learning, physics-informed) for physical and experimental systems to create digital twins of scientific campaigns.
Role type
Co-op Machine Learning Engineer (Scientific AI)
Builds
Calibrated, uncertainty-aware digital twin models for Lila's AI Science Facilities (AISF)
Domain
Physical sciences, life sciences, scientific computing, experimental systems
Deliverable
production ML models
Required skills
Python, PyTorch/JAX/TensorFlow, operator learning, surrogate modeling, physics-informed ML, uncertainty quantification, model calibration, scientific computing, experiment tracking
Preferred skills
Fourier Neural Operators, DeepONets, graph neural operators, transformer-based operators, active learning, Bayesian optimization, materials science applications
Technologies
PyTorch, JAX, TensorFlow, Python
Responsibilities
Build and train surrogate/operator-learning models against experimental/simulation data; Calibrate models and quantify uncertainty against active campaign data; Frame scientific questions as concrete ML tasks with baselines; Document findings and present results cross-functionally
Seniority
Co-op (Master's/PhD student)