Lila Sciences
Biotech
Co-op,MachineLearningforDigitalTwins
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Co-op, Machine Learning for Digital Twins at Lila Sciences. Skills: Machine Learning, Digital Twins, Operator Learning, Uncertainty Quantification. Build ML models. Train ML models”
What You'll Achieve.
Higher-throughput experiments; Higher-quality experiments; Inform experiment design; Inform experiment run
Industry & Context.
Scientific questions to ML tasks
What They're Looking For.
Must Have
Master's or PhD, Python programming skills, ML frameworks experience, ML to scientific systems, Open-ended questions to ML tasks, Model training, validation, debugging, Experiment tracking, performance evaluation, Messy, heterogeneous, or evolving datasets, Collaborating across teams
Nice to Have
PhD preferred, Modern operator-learning methods, Digital twins experience, Model update, calibration, uncertainty-aware modeling, Closed-loop scientific decision-making, Physical science applications
What You'll Do.
Contribute to ML models
Build surrogate models
Train surrogate models
Build operator-learning models
Train operator-learning models
Build physics-informed models
Train physics-informed models
Validate against data
Frame scientific questions
How You'll Work.
Team & Collaboration
Cross-departmental collaboration; ML, software engineering, physical science teams
Communication Scope
Write-ups; Presentations
Process & Methodology
Experiment tracking
Full Job Description
Your Impact at LILA Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Within Physical Sciences AI, our team partners with the diverse experimental groups to build digital twins of experimental campaigns, focusing on calibrated, uncertainty-aware models that enable higher-throughput, higher-quality use of Lila's AI Science Facilities (AISF). As an ML for Digital Twins Co-Op, you will work on building, training, and evaluating ML models for physical and experimental systems. You will get hands-on experience with operator learning, surrogate modeling, and uncertainty quantification, shipping work that directly informs how next-generation AISF experiments are designed and run. What You'll Be Building 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, with mentor guidance Calibrate models, quantify uncertainty, and validate against data flowing 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 in cross-departmental collaboration through write-ups and presentations What You'll Need to Succeed Pursuing a Master's or PhD in Machine Learning, Computer Science, Applied Mathematics, Physics, Materials Science, Chemical Engineering, Mechanical Engineering, Electrical Engineering, or a related quantitative field (PhD preferred) Strong programming skills in Python and hands-on experience with ML frameworks such as PyTorch, JAX, TensorFlow, or similar Experience applying machine learning to scientific, engineering, physical, or experimental systems Familiarity with neural operators, operator learning, spatiotemporal modeling, field prediction, dynamical systems, scientific computing, surrogate modeling, or physics-infor
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