Doctoral student position in AI for Data Management at the Department of Computing science, Umeå University.
The project, led by Professor Diego Calvanese, concerns foundational and/or applied research in the context of flexible and efficient management of large amounts of richly structured data, by relying on the paradigm of Virtual Knowledge Graphs (VKGs, also known as Ontology-based Data Access). Specifically, the project aims at extending VKGs to address novel settings, that may include the following elements:
- (i) additional forms of data/knowledge (e.g., temporal, geospatial, aggregated, numeric);
- (ii) heterogeneous data sources of different types beyond relational ones (such as graph-structured data, json, xml, csv, streaming, textual);
- (iii) novel kinds of problems, notably provenance, explanation, personalization, data analysis and machine learning over VKGs, optimization and performance tuning; and
- (iv) novel applications of VKGs (e.g., industry 4.0, smart cities, e-health).
Depending on the qualifications and interests of the student, the PhD research topic can include one or more of the above extensions of the traditional VKG paradigm, and can be targeted more towards foundational and theoretical research or more towards applied and experimental research.