AI & modeling
Develop and evaluate models for prediction, simulation, and scientific research.
- Predictive models. Machine learning for scientific datasets, including molecular properties and biological measurements.
- Model evaluation. Compare approaches, benchmark performance, and assess uncertainty and limitations.
- Experiment prioritization. Use active learning to guide experiments and generative models to explore molecular candidates.
- Scientific simulation. Quantum chemistry and mathematical models for molecular, transport, reaction, and biological systems.
Example deliverables
Feasibility assessment, model benchmark, trained prototype, prioritized experiment list, or documented simulation.
