Development of analytical workflows that use machine learning and AI to identify patterns, classify observations, and extract useful information from scientific datasets.
Work can include preprocessing, exploratory analysis, feature extraction, supervised and unsupervised learning, evaluation, and interpretation.
It can be applied to experimental measurements, biological datasets, environmental observations, imaging, signals, and other scientific data.
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.