The research programme focuses on the development and application of machine learning and natural language processing methods for advanced data analysis in the field of nutritional science. The aim of the project is to develop innovative approaches for integrating, structuring, and interpreting heterogeneous data sources, such as scientific publications, food and nutrition databases, clinical records, and related datasets.
The young researcher will develop models for automated knowledge extraction from textual sources (e.g., named entity recognition, relation extraction, and ontology construction), classification and prediction of dietary patterns, and personalized recommendations. The research will support the development of tools for automated dietary intake analysis, diet quality assessment, and the discovery of associations between nutrition and health.
An important part of the research will focus on validating the developed methods using real-world data and collaborating with partners in medicine, public health, and food science.