Probabilistic Numerics for an ODE-Constrained Battery Model
Overview
What this challenge is about.
Use a probabilistic ODE solver and hierarchical inference to fit battery degradation posteriors across a fleet. Earn a verifiable certificate.
The scenario
The startup (around 18 staff, pre-Series A) has 4 fleet pilots running and needs an uncertainty story before customers will commit to a paid contract; deterministic fits have failed the buyer's risk-management review twice.
The Brief
What you'll do, and what you'll demonstrate.
Replace a deterministic ODE-fit with a probabilistic-numerics-based hierarchical Bayesian fit that produces calibrated joint posteriors over parameters and trajectories.
Earning criteria — what you'll demonstrate
- Integrate probabilistic ODE solvers into a Bayesian-inference workflow
- Build hierarchical priors that share information across related units
- Validate posterior calibration via predictive coverage on held-out data
- Communicate uncertainty in physics-based ML to a non-academic audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Probabilistic Numerics
Apply probabilistic numerics to solve real industry problems and demonstrate production-level capability.
- Bayesian Inference
Apply bayesian inference to solve real industry problems and demonstrate production-level capability.
- Ode Modeling
Apply ode modeling to solve real industry problems and demonstrate production-level capability.
- Hierarchical Models
Apply hierarchical models to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Uncertainty Quantification
Apply uncertainty quantification to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Research Scientist
Integrating probabilistic numerics with hierarchical Bayesian inference on a real engineering problem is portfolio-grade work for industrial research scientist roles.
This challenge sharpens
- probabilistic-numerics
- hierarchical-models
- ode-modeling
Applied AI Scientist
Translating Bayesian posteriors into a fleet-facing RUL story is exactly the work applied AI scientists do at industrial-AI startups.
This challenge sharpens
- bayesian-inference
- uncertainty-quantification
- ode-modeling
ML Researcher
Choosing solver priors, justifying pooling structure, and reporting calibration are the rigor signals ML research teams look for.
This challenge sharpens
- probabilistic-numerics
- bayesian-inference
- hierarchical-models