Human knowledge is inherently multi-modal, and it is more than just a collection of isolated pieces of information, irrespective of the form of expression. Instead, it emerges from the interconnectedness of all of these information fragments. Knowledge graphs are a powerful way of capturing such interconnected knowledge. Such graphs are effective for storing and relating information that can easily be expressed in textual form, by assigning a simple text label to every node in a graph or relating them to literals represented using strings or blobs. However, they so far fail to capture the richness of information that is not easily expressed as a short piece of text.

The ‘MediaGraph' project aims to extend the concept of a knowledge graph with all types of information which are currently representable as multimedia to be able to capture the richness of human knowledge. In contrast to a knowledge graph whose nodes are associated with a textual label (with a specification arising from relations to other entities and labels), the nodes in a media graph will be able to represent and interrelate any part of any multimedia document. The resulting graph will not only describe the semantic but also the stylistic and technical relations between the documents and their components and form the basis for novel media interaction paradigms.

For this project, we are seeking a motivated PhD student to help make MediaGraph a reality. Requirements include an MSc in Computer Science or a related discipline, a background in both theoretical and applied aspects of computer science as well as a passion for discovering new things. Experience in the areas of databases, data management, semantic web technologies, multimedia processing, multimedia analysis, machine learning, and/or signal processing is considered a plus. The PhD Student will be in charge of the development of manual as well as automated construction methods for MediaGraphs and will define and own some of the practical use-cases to which MediaGraph will be applied in practice. They will also contribute to the design and implementation of representation, querying, and evaluation mechanisms for the graphs.

To apply, please gather your curriculum vitae, all grade transcripts, selected publications (if available), a list of at least three references, and your BSc/MSc theses as PDF files and go to https://www.apply.dsi.uzh.ch/position/5996546.

The University of Zurich is committed to enhancing the number of women in scientific positions and, therefore, particularly invites women to apply. Women who are as qualified for the position in question as male applicants will be given priority.

For more information on the project and the research group, visit https://www.ifi.uzh.ch/en/ddis.html.