Tallinn University seeks to hire two Research Fellows in Cultural Data Analytics, particularly in Audiovisual Machine Learning, and Cultural Dynamics, to work on ambitious, high-impact research at the CUDAN ERA Chair project CUDAN.
Start of the employment contract: 01.07.- 01.09.2021, duration of the contract is up to 31.12.2023. Deadline of submitting the application documents is 31st May, 2021.
Successful candidates will join the CUDAN ERA Chair project in Cultural Data Analytics, which is funded by the European Commission in the Horizon 2020 research and innovation program with 2.5 million Euro (Grant no. 810961). The selected fellows will complement a highly multidisciplinary research group, currently consisting of 4 faculty, 4 senior fellows, and 5 PhD Students. Current expertise includes art history, algorithmic curation, cultural history, cultural semiotics, computational linguistics, computer science, computational social science, creative industry studies, digital education, evolutionary economics, generative art, machine learning, media innovation, media policy, and network science. Bridging three university schools, the research group collaborates with high-profile academic partners that are spread across the globe, while cooperating with several public and private stakeholders. These include libraries, museums, media institutions, and open data providers. The research group activity forms the core of the CUDAN Open Lab, performing research while simultaneously functioning as a forum for intellectual exchange and meaningful academic mixing. Together, our aim is to systematically deepen our understanding of cultural interaction and dynamics from deep history to the present. We harness a rare high-risk/high-gain opportunity of combining cutting-edge computation, quantification, qualitative inquiry, critical and creative aesthetics, in close collaboration and co-authorship. Our core mission is to produce and publish exemplary proofs of concepts in high-impact papers and to act as an incubator for follow-up projects and applications, while cross-fertilizing knowledge and methods across disciplines and with external stakeholders. Successful candidates will be provided with data-science-grade laptops, conference travel funds, and opportunities to collaborate with international partners. The lab environment provides ergonomic co-working spaces with individual adjustable desks and secondary screens, large blackboards, state-of-the-art projection, video conferencing and HPC equipment. The working language of the CUDAN research group is English. For more information regarding our mission, group members, and ongoing events see
https://cudan.tlu.ee.
Requirements for the candidate (incl professional experience)
- A PhD in a computational or quantitative area, such as computer science, information science, network science, physics, or mathematics, while demonstrating understanding or meaningful interest in socio-cultural phenomena. Alternatively, a PhD in a cultural research area, such as art history, computational linguistics, digital humanities, with a strong track record in computation or quantification.
- Demonstrated ability to produce (potentially) high-impact research and work with large-scale (socio-cultural) data, including corpora of visual and audiovisual materials, or capturing the structure and dynamics of multidimensional spaces of (cultural) meaning.
- Either strong skills in audiovisual machine learning, in at least one of the following areas: Computer vision, deep learning, or pattern recognition; image segmentation & feature classification regarding objects, scenes, faces, poses, textures, etc. (aka iconography); 2vec or embed-everything approaches; latent-space cartography; multidimensional data analysis & visualization (manifold learning, diffusion maps, etc.); or another meaningful state-of-the-art application of machine learning that can be useful to make sense of large-scale visual or audiovisual data.
- Or strong quantitative and computational skills, in at least one of the following areas: Data science, information science, or computational social science; socio-physics or reality mining; complexity science or network science; multilayer and temporal network analysis; higher-order graphs or topological data analysis; mathematical modeling (including ecology or socio-cultural dynamics); matrix cluster analysis (as found in network neuroscience, DNA microarray-analysis, or systems biology); multidimensional flow analysis or fluid dynamics; or another meaningful state-of-the-art application of computation and quantification that can be useful for making sense of cultural dynamics.
Desired skills
– Experience in the acquisition and processing of large (cultural) datasets, including familiarity with data dumps, APIs, scraping, streaming, knowledge graph queries, and data integration.
– Advanced programming skills in at least one widely used language (e.g. Python, R, Julia, Processing, Javascript/D3, C++, or equivalent), including proficiency with libraries for data manipulation and analysis, machine learning, etc.
– Strong visual literacy and data visualization skills. Ability to read and produce high-quality scientific figures as found in multidisciplinary journals. Designing dynamic visualizations and interactive experiences is a plus.
– Preference will be for candidates who possess multidisciplinary competence and the necessary open-mindedness to bridge the so-called two worlds, where understanding implies qualification of specific complications and quantification of emerging complexity.
The CUDAN ERA Chair project and Tallinn University is committed to gender, age, cultural, geographic, and disciplinary diversity.
Job responsibilities
– Performing research in co-authorship with CUDAN research group members and external partners under the supervision of the CUDAN ERA Chair holder, with typical activities including research design, data acquisition, data preparation, data analysis, visualization, and the composition of figures and manuscripts.
– Publishing and assessing research results, ideally aiming for high-profile multidisciplinary journals and conferences.
– Participation in CUDAN Open Lab activities, including discussions with students, faculty, and external stakeholders, mutually building and sharing expertise, developing novel research ideas, and supporting the application for additional funding.
– Co-supervising and teaching CUDAN students.
– Openness and readiness to engage in multidisciplinary translation, within a diverse and international research group.
Additional details:
https://www.tlu.ee/en/taxonomy/term/84/research-fellow-cultural-data-analytics-0
