REMINDER: One week left to apply!
Please note that the URL of the job ad has changed in the meantime (correct link appears in the text below). Don't hesitate to get in touch with me if you have any questions.
The project “Reading concordances in the 21st century (RC21)”, run jointly by Friedrich-Alexander-Universität Erlangen-Nürnberg and the University of Birmingham is looking for a
POSTDOCTORAL RESEARCHER IN COMPUTATIONAL LINGUISTICS (100%, E13 TV-L, starting ASAP)
Application deadline: 16 Dec 2022 Interviews: week beginning 19 Dec 2022 (held via zoom) Format of application: by email to stephanie.evert@fau.de (including evidence of all relevant qualifications, preferably in a single PDF) Start of position: 1 Feb 2023 Duration of employment: until 31 Jan 2025 Placement: Computational Corpus Linguistics group (www.linguistik.fau.de) Contact / queries: Stephanie Evert
https://www.jobs.fau.de/jobs/postdoctoral-researcher-in-computational-lingui...
This is the first of two postdoctoral positions. The second post, which will be based at the University of Birmingham, will be advertised in the new year, with a starting date in the spring.
PROJECT INFO In today's digital world, the amount of text communicated in electronic form is ever-increasing and there is a growing need for approaches and methods to extract meanings from texts at scale. Corpus linguists have long been studying recurring patterns in digitised texts with the help of concordances, i.e. displays that show many occurrences of a word, phrase or construction across a range of contexts in a compact format. However, lacking a well-established and clear-cut methodology, the art of reading concordances has not yet realised its full potential. At the same time, there has been very little innovation in algorithms in the concordance software packages available to corpus linguists. This project proposes an innovative approach to reading concordances in the 21st century. Through the collaboration between the University of Birmingham and Friedrich-Alexander-Universität Erlangen-Nürnberg we combine strengths in theoretical work in corpus linguistics with expertise in computational algorithms in order to develop a systematic methodology for reading concordances and corresponding algorithms for the semi-automatic analysis of concordance lines. Through two case studies on English and German data sets, we will establish an approach that not only provides innovation in corpus linguistics, but also has wider implications for the analysis of textual data at scale, while still retaining a humanities perspective.
RESPONSIBILITIES OF THE POSTDOCTORAL RESEARCHER • Developing, implementing, and applying novel computer algorithms to support and improve the manipulation of concordance displays • Producing documentation • Carrying out interdisciplinary case studies using the new methodology in close collaboration with other researchers • Leading on the organisation of workshops and other dissemination activities • Developing training materials for workshops and materials for web presence • Analysing data, writing up results, and co-authoring publications • Managing project tasks, supervising student assistants, and providing progress reports • Managing collaborative activities with the UK team
REQUIRED QUALIFICATIONS • PhD or DPhil in Computational Linguistics, Computer Science, Machine Learning, Corpus Linguistics, Computational Humanities, or similar subject (completed or near completion) • Evidence of strong programming skills, ideally in Python • Evidence of ability to analyse linguistic data • Native-like language skills in German and English • Excellent communication skills in English, including the ability to write for publication, present research proposals and results, and represent the project team at meetings and research events • Ability to work independently, manage own academic research and associated activities, and to supervise student assistants
OPTIONAL QUALIFICATIONS • Strong publication record (commensurate with opportunities and experience) at relevant international conferences (computational linguistics, DH, corpus linguistics) • Experience in interdisciplinary research is a plus • Experience with deep learning approaches and/or statistical methods is a plus