We are seeking the opportunity to hire an introductory group of up to 5 PhD students to work on interdisciplinary computational social science projects under the guidance of CSS Lab core faculty. The core teaching/supervisory team includes:
- Dr. Olga Boychak (School of Arts, Communication and English, Faculty of Arts and Social Sciences)
- Professor Monika Bednarek (School of Humanities, Faculty of Arts and Social Sciences)
- Professor Eduardo G. Altman (School of Mathematics and Statistics, Faculty of Natural Sciences)
- Professor Kalervo Gulson (School of Education and Social Work, Faculty of Arts and Social Sciences)
- Associate Professor Tristram Alexander (School of Physics, Faculty of Natural Sciences)
- Dr Aim Sinpeng (School of Social and Political Sciences, Faculty of Arts and Social Sciences)
- Dr Joan Gray (School of Arts, Communication and English, Faculty of Arts and Social Sciences)
All projects will draw on the existing strengths and expertise of the Social Media and Data Science Research Group and associated faculty around modeling and analyzing data-based representations of individuals and communities and algorithmic biases associated with these emerging modes of production of knowledge. The core faculty brings significant research expertise in three distinct areas of computational social science: language and topic modeling (Bednarek, Boichak), network analysis and community discovery (Altmann, Alexander), machine learning and artificial intelligence (Sinpeng, Gray, Gulson ). Coming from different disciplines in two faculties, each has demonstrated extensive research leadership in terms of capabilities as well as successfully innovated in the HDR research training space.
We invite applicants with a background in computational social sciences, digital humanities, and/or data science with strong regional and/or district expertise. The successful candidate will join an interdisciplinary research team at the laboratory, which is located in the interdisciplinary, intensive research environment of the Sydney Center for Advanced Research in the Social Sciences and Humanities (SSSHARC) at the University of Sydney. The supervisory team usually consists of a supervisor and an associate supervisor from different disciplines. This will expose PhD candidates to different traditions in computational social science, allow them to tackle challenging research questions and contribute to the creation of long-term multidisciplinary collaborations within the university.
Successful projects will use a range of approaches to critically understand big data and computing in their socio-technical context. We specifically seek projects that involve creating, implementing, testing, or evaluating a range of computational approaches (eg, data visualization, corpus linguistics, topic modeling, network analysis, statistical machine learning) to explore a variety of potential datasets (including those collected from social media, news media, political documents, etc.) and answer important theoretical and empirical questions to address socially relevant issues in contemporary society.
The theoretical social science frameworks that underpin the project may come from a range of social and humanities disciplines. Of particular interest are projects that explore the development of algorithmic systems and policies and critically examine their social, cultural and ethical implications. Projects can cover a wide range of social fields, including but not limited to human rights, public discourses, digital literacy, safety and security, educational outcomes, social studies of science, etc. Computational methods can be combined, enhanced, interrogated, or enriched with traditional methods by triangulating approaches.