The Institute for Natural Language Processing (IMS) at University of Stuttgart has an opening for a doctoral researcher (or a postdoc) in the context of project CAUTION [1] to work on the operationalization of concepts from the field of narratology and on tools for computer-aided analysis of a corpus of literary texts. CAUTION is a collaboration between literary studies and computational linguistics, headed by Janina Jacke (University of Göttingen) and Jonas Kuhn (University of Stuttgart) [2], which addresses the phenomenon of unreliable narration in fiction. The project aims to advance the representational means for capturing this phenomenon, to devise a framework for intersubjectively stable annotation in texts, and to develop computational tools for automatically detecting signals for unreliable narration in a corpus. The project is funded by DFG (the German Research Foundation) and is associated with the Priority Programme SPP 2207 Computational Literary Studies.

The successful candidate will work (i) on the detection of text properties signaling unreliable narration using data-driven techniques from natural language processing (NLP) and (ii) on capturing systematically what reasoning leads a reader to consider the narrator of a story to be unreliable, using a symbolic knowledge representation & reasoning framework. Specifically, the project explores how the interaction between story-internal knowledge and background knowledge from various sources can be formalized in a belief-desire-intention model for intelligent agents and how such a formalization can be integrated with the practice of text annotation. An important element of the project will be a close exchange between theoretical research in literary studies and research on algorithmic modeling techniques. 

The candidate must have a Master's degree in computational linguistics, computer science, digital humanities, or similar. Familiarity with the data-driven modeling paradigm in current NLP research, programming skills and experience in running and evaluating corpus-based modeling experiments are a prerequisite. A reading knowledge of German is desirable, since we work on a corpus of German texts. Some background in digital humanities on the one hand, and in knowledge representation & reasoning, declarative programming (e.g. Prolog) and/or multi-agent systems on the other hand would be a plus. 

IMS Stuttgart offers an inspiring environment for interdisciplinary work on algorithmic text analysis in a range of research contexts; several groups work on digital humanities and computational social science projects.

The position will be available for three years, starting in November 2021. All applications received until October 5, 2021 will receive full consideration, but the position will remain open until filled. Do not hesitate to get in touch when you find this opening after October 5.

The salary for a doctoral researcher working on the CAUTION project is according to the German university payscale (TV-L 13 65%, see [3] for details). By contributing to teaching at the department, the 65% contract can be increased to 100% TV-L 13, which can make the project position attractive to postdoctoral researchers with the relevant qualifications. To apply, please send a CV and letter of motivation in a single PDF document to Jonas Kuhn, jonas.kuhn at ims.uni-stuttgart.de.  Please include names of 2-3 individuals willing to write a letter of recommendation. For inquiries regarding the position, please also contact Jonas Kuhn.

The University of Stuttgart aims at increasing the number of women in academia. Therefore, women are particularly encouraged to apply. Applications of severely disabled candidates with equivalent qualifications will be given priority. 

Information according to article 13 of the General Data Protection Regulation (GDPR) for job application processes at University of Stuttgart can be found here: https://www.zendas.de/zendas/informationen_bewerbung.html and https://www.uni-stuttgart.de/en/privacy-notice/job-application/. 

[1] https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-Caution/
[2] https://www.ims.uni-stuttgart.de/institut/arbeitsgruppen/gcl
[3] https://oeffentlicher-dienst.info/c/t/rechner/tv-l/west?id=tv-l-2021