[Commlist] Tallinn University seeks 5 research fellows in cultural data analytics

Job opening at Tallinn University for 5 Research Fellows in Cultural 
Data Analytics

Tallinn University has announced an open competition for five positions 
of Research Fellow in Cultural Data Analytics in H2020 funded ERA Chair 
project CUDAN.

Start of the employment contract is negotiable: between 15.06.2020- 
01.09.2020.

The duration of the contract is 3 years.

Funded through the European Commission, the designated CUDAN ERA Chair 
holder, Professor Maximilian Schich, together with the CUDAN project 
team, the Baltic Film, Media, Arts and Communication School, the School 
of Humanities, and the School of Digital Technologies at Tallinn 
University, is looking for research fellows in the area of Cultural Data 
Analytics to deepen our understanding of the nature of cultural 
interaction, cultural dynamics, and cultural evolution, doing research 
while nurturing multidisciplinary cross-fertilization. Through this 
recruiting, the CUDAN project will bring together a group of at least 5 
research fellows and 5 PhD students to harness the rare 
high-risk/high-gain opportunity of combining multidisciplinary science, 
computation, information design, with art and cultural history, cultural 
media studies, and cultural semiotics, in close collaboration and 
co-authorship. The newly established research group will form the core 
of the CUDAN Open Lab, which, in addition to research, aims to function 
as a forum for intellectual exchange, and as an incubator for follow-up 
projects. Ideally, the research fellows contribute aspects of network 
science, complexity science, science of science, computational social 
science, machine learning/AI, information science, data science, data 
visualization, user experience design, and/or digital humanities to the 
locally existing expertise in art and cultural history, cultural media 
studies, cultural semiotics, and digital technology. Beyond the local 
environment, the CUDAN initiative will also provide the research fellows 
and PhD students with the opportunity to work closely with high-profile 
external partners in multidisciplinary science, cultural heritage 
institutions, and stakeholders in the cultural industries in Estonia, in 
Europe, and around the globe. More information on the CUDAN project, see 
http://cudan.tlu.ee/.

We are particularly interested to work on the following research challenges:
	• Using machine learning to analyze images and/or audio-visual material 
over historical time scales, to reveal patterns and biases in large data 
collections through a kind of “artificial neural science”.
	• Using linguistic topic modelling and/or bi-partite network science to 
analyze the structure and evolution of large corpora of texts and/or 
classifications, feeding into a “palaeontology of memes”.
	• Using temporal multilayer network analysis and/or topological data 
analysis to make sense of large cultural knowledge graphs, through 
capturing fundamental emerging patterns of “network multiplicity”.
	• Combining the analysis of multimedia, unstructured, and structured 
data in a so-called embed-everything-approach that could result in a 
kind of “multidimensional fluid-dynamics of meaning”.

In addition, we aim to nurture the Cultural Analytics community through 
addressing the following challenges:
	• Mapping and characterizing the achievements, opportunities, and 
limits of Cultural Data Analytics, ideally resulting in actionable maps 
of the relevant “multidisciplinary ski area”.
	• Enabling and optimizing the CUDAN Open Lab experience through a 
conscious effort of user experience design, observing and designing 
workflows, events, and other forms of intentional “academic mixing”.

Candidates that are motivated and have the capacity to spearhead one of 
these aspects are strongly encouraged to apply!

‍Requirements for the candidate (incl. professional experience)
	• The ideal candidate has a PhD in a field closely related to Cultural 
Data Analytics, with expertise in quantification, computation, and/or 
visualization, ideally including previous working experience with 
large-scale socio-cultural data.
	• The individual focus could be in machine learning/AI, network 
science, topological data analysis, complexity science, science of 
science, computational social science, information science, data 
visualization, user experience design, and/or digital humanities.
	• Candidates with a PhD in a seemingly unrelated field whose methods 
could nevertheless be valuable to the CUDAN project will be considered: 
Examples include experts in socio-physics, computer vision, species 
niche modeling, time-series analysis, or matrix clustering as found in 
systems biology and neuroscience.
	• The working language of the CUDAN research group is English.

Load: 1,0 The duties are approximately divided in (1) research (90%), 
(2) teaching (5%) and (3) internal and external service (5%).
Salary: to be agreed, but internationally competitive.
Location: Tallinn, Estonia.
Language skills: English C1,  Estonian is not required; if staying 
longer in Estonia, the candidate would need to acquire Estonian as a 
working language within 3 years in order to be able to participate in 
administrative tasks.

See the more detailed job advert on Tallinn University webpage: 
https://www.tlu.ee/en/taxonomy/term/84/research-fellow-cultural-data-analytics#required-application-documents

Additional information about the posts and the application process: 
please refer your administrative questions to (konkurss /at/ tlu.ee) and 
questions on content to project coordinator for CUDAN Mariliis 
Niinemägi, (mariliis.niinemagi /at/ tlu.ee)

---------------
The COMMLIST
---------------
This mailing list is a free service offered by Nico Carpentier. Please use it responsibly and wisely.
--
To subscribe or unsubscribe, please visit http://commlist.org/
--
Before sending a posting request, please always read the guidelines at http://commlist.org/
--
To contact the mailing list manager:
Email: (nico.carpentier /at/ vub.ac.be)
URL: http://nicocarpentier.net
---------------