Development of predictive and computational models for scientific variables, processes, and outcomes.
Models may use statistical learning, machine learning, regression, classification, time-series methods, or domain-specific computational approaches.
Applications include environmental prediction, biological modeling, chemical property prediction, neuroscience, and engineering problems.
Implementation is selected according to the available data, technical constraints, required level of automation, and the existing software or research environment. The solution can be implemented as a standalone component or integrated into a larger system.
The resulting system is intended to provide a clear computational workflow that can be evaluated, maintained, and extended as the project develops. Model choice, data processing, interfaces, and deployment can therefore be adapted to the requirements of the specific project.