Readers of this blog might be interested in this job opportunity.
Via David Zbíral
Four research fellowships in an ERC project on medieval heresy and inquisition
Perhaps some of you or your contacts might be interested in the following listings in the ERC Consolidator Grant-funded Dissident Networks Project (DISSINET, https://dissinet.cz), using computational methods to make sense of medieval religious dissidence and inquisition.
All positions are full-time, with a 5-year perspective (based on performance review), and they start with the launch of the project on 1 September 2021 (negotiable).
1) NLP specialist / computational linguist (esp. focusing on Latin)
https://www.muni.cz/en/about-us/careers/vacancies/60994
Deadline: 30 April 2021
2) Geospatial data analyst
https://www.muni.cz/en/about-us/careers/vacancies/61254
Deadline: 30 April 2021
3) Data scientist
https://www.muni.cz/en/about-us/careers/vacancies/61774
Deadline: 30 April 2021
4) Social scientist focusing on social network analysis
https://www.muni.cz/en/about-us/careers/vacancies/61454
Deadline: 17 May 2021
DISSINET studies the social, spatial, and discursive patterns of medieval dissidence and inquisition, especially through social network analysis, geospatial data analysis, and computational text analysis / NLP. Historians in the team transform medieval sources into rich structured data on human interactions in dissident religious cultures of the past, on inquisitorial trials, and on inquisitorial records. These data, with social networks ranging usually from ca. 30 to 1,000 persons, represent a rich source of information on pre-modern social interactions and allow us to test specific hypotheses derived from both social scientific and historical research, and theorize pre-modern social networks as well as the functioning of religion in Europe in the 13th to 16th centuries. The target volume of manually collected data is ca. 20,000 persons, 5,000 locations, 200,000 richly structured statements, and 2,000,000+ individual data points. Another extensive layer of data will be provided by natural language processing.
Archaeological Networks 