Application of machine learning methods to scientific datasets and problems where domain structure, physical constraints, or scientific interpretation matter.
Projects can involve feature engineering, model development, validation, uncertainty analysis, explainability, and integration with computational models.
Applications span biology, chemistry, environmental science, neuroscience, engineering, and other computational research domains.
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.