The NIKAW project aims to leverage textual data from the ancient world to reconstruct the transmission of knowledge across multilingual, geographically dispersed, and chronologically extended communities. Bringing together expertise from history of ideas, knowledge circulation, book history, reception studies, social network analysis, knowledge network analysis, and bibliometrics, the project seeks to analyze how knowledge was shared and transformed in the ancient world (ca. 8th century BCE – 4th century CE).
The postdoctoral researcher will join an interdisciplinary team working at the intersection of computational linguistics, intellectual history, and AI-driven knowledge analysis. The position is embedded in the Centre for Computational Linguistics (CCL) within the Department of Linguistics, which conducts cutting-edge research in computational linguistics, with a strong focus on language technology applications. The researcher will also engage with the digital humanities (DH) community at the Faculty of Arts, participating in monthly seminars, drop-in sessions, and other collaborative initiatives.
Additionally, the researcher will collaborate with scholars in the Faculty of Economics and Business (FEB), gaining access to expertise and methodologies in bibliometric and network analysis. The researcher will also benefit from the expertise and resources of LECTIO, KU Leuven’s research institute for pre-modern intellectual history, and Leuven.AI, KU Leuven’s institute for artificial intelligence research.
Responsibilities
- One PhD researcher is developing large-scale named entity recognition and disambiguation for an Ancient Greek and Latin corpus.
- The other PhD researcher is conducting Social Network Analysis (SNA) on a manually annotated subset of the corpus.
The postdoctoral researcher will advance the project’s methodological development by focusing on one or more of the following areas:
- Heterogeneous Graph Construction: Develop a structured graph that links authors to the passages (text entities) they have written, as well as to the individuals (including other authors) mentioned in the texts.
- Network and NLP Integration: Combine NLP-derived embeddings with relational network structures. Leverage topological features from the network to refine textual representations of authors and texts.
- Graph Representation Learning: Investigate the stability and reliability of graphs derived from textual information. Apply graph representation learning techniques to process networks with diverse entity types.
- Knowledge Flow Modeling: Use textual networks of mentions to model the circulation of knowledge as a dynamic flow influenced by historical events.
Profile
- A PhD in a relevant area (social network analysis and/or NLP) with peer-reviewed publications in the domain.
- A strong interest in and own vision for the outlined responsibilities.
- Familiarity with core technologies, such as language models and graph databases.
- Experience in guiding junior researchers in interdisciplinary projects.
- The ability to lead scientific inquiries, whether predefined or emerging during the project.
Offer
- Duration: 1.5 years full-time position.
- Remuneration: Salary scale 44.
- Opportunities: Access to a large amount of unexploited data, a stimulating environment, and the application of state-of-the-art techniques.
- Professional Growth: A collaborative and dynamic research environment with opportunities for professional development.
- Research Freedom: Flexibility to orient the research project based on your background.
- Community: Integration into the DH and AI community at KU Leuven.
- Teaching: The position is research-only, without teaching obligations, but teaching opportunities can be arranged if desired.
Interested?
KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address.
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