DMRC EOIs 2020 Annual Scholarship Round

Annual scholarship round 2020 – DMRC Expressions of Interest

HDR Scholarships are available to competitive applicants with an excellent academic track record through QUT’s Annual Scholarship Round, which closes in October 2020 for higher degree research starting in 2021.

We are inviting Expressions of Interest for projects that align to any of our five research programs .  In 2020, we have additional interest in projects that align to the objectives of the Australian Research Council Centre of Excellence for Automated Decision Making and Society (ADM+S), for which the DMRC is a major node. ADM+S involves three of our programs: Computational Communication & Culture; Digital Social Contract; and Digital Publics.

Carefully read the relevant program information and consider how your proposed research will align before submitting an EOI. Also consider information available on the ADM+S website  if your work aligns to one of the relevant programs.

You will find the names of prospective supervisors under each of our program descriptions. Although we invite you to name a preferred supervisor, we cannot guarantee they will be available. In part, our decision about your EOI will depend on if we can match you with a suitable supervision team.

To lodge a DMRC EOI for the scholarship round, complete this online form. If you are unable to access the online form, contact

Please note that QUT has new procedures for the scholarship round in 2020.  To apply, you will need to undertake the following steps:

  1. Complete the DMRC Expression of Interest form. The closing date for DMRC EOIs is August 31st.
  2. We will review your EOI, considering your previous academic experience, your project proposal’s alignment to a DMRC program, and supervisor availability. At this stage we may let you know that the DMRC cannot support your proposal. In some instances, we may refer your EOI to another part of QUT, which may be better aligned.
  3. If we contact you to let you know we can support your application, you will need to submit a formal application to QUT’s Online System, with your prospective supervisor’s support.  The formal application involves two steps:  A) You will need to complete another EOI – for the University this time, and you will be asked to provide your academic transcripts at this stage, for an eligibility check. The due date for Step 1 is 20 September 2020 (11.59 AEST). B) If you are deemed to be eligible for entry into the PhD, you will be asked to complete the next phase of your application. This is due by October 30 2020 (11.59 AEST).
  4. Your formal application will be considered alongside other applicants through a competitive ranking process to determine if you will be offered a scholarship.

Concurrent scholarship opportunities

In addition to the scholarships available through the annual scholarship round, we currently have additional scholarships available, aligned to the following  funded projects.

Expressions of Interest for these projects will be considered as part of the Annual Scholarship round – so please follow the procedure above. When completing the DMRC EOI, please clearly indicate which project you are expressing interest in.

Communicating Scientific Knowledge through The Conversation

(Supervisors: Jean Burgess, Axel Bruns, Kim Osman)

Funded by a Canadian Social Science and Humanities Research Council Partnership Grant hosted by the University of British Columbia, this PhD researcher will work as part of a global team to investigate the role of The Conversation and similar platforms in communicating scientific knowledge to a broader public. By enabling scholarly experts to share their knowledge in a journalistic format, and providing content for free republication in mainstream media, The Conversation creates a potential for expert voices to engage in public debates. Working with internal Conversation data as well as data on news and social media content, this project investigates whether and how far such content circulates in public debates; what issues, topics, and fields achieve the greatest public impact; and what consequences this has for the standing of the scholarly authors contributing to The Conversation. Ideally, candidates for this PhD position should be equally familiar with qualitative, close reading as well as quantitative, computational research methods.

Evaluating the Challenge of ‘Fake News’ and Other Malinformation
(Supervisors: Axel Bruns, Stephen Harrington, Dan Angus)

Funded by an ARC Discovery project, this PhD researcher will use qualitative and quantitative analytics methods to investigate the dissemination patterns and processes for mis- and disinformation. They will draw on the state-of-the-art social media analytics approaches to examine the role of specific individual, institutional, and automated actors in promoting or preventing the distribution of suspected ‘fake news’ content across Australian social media networks. Building on this work, they will develop a number of the case studies of the trajectories of specific stories across the media ecosystem, drawing crucially on issue mapping methods to produce a forensic analysis of how particular stories are disseminated by a combination of fringe outlets, social media platforms and their users, and potentially also by mainstream media publications. Ideally, candidates should be equally familiar with qualitative, close reading as well as quantitative, computational research methods.

Evaluating machine vision methods for social media data

(Principal Supervisor: Dan Angus)

Funded through our ARC Discovery project Using machine vision to explore Instagram’s everyday promotional cultures, this PhD researcher will develop and examine the use of contemporary machine learning approaches within the domain of social media. Artificial intelligence approaches used by platforms are rapidly advancing, and now include machine vision algorithms to automatically classify faces, expressions, objects, and brand logos in images. The results are used to provide targeted content to users, often without their knowledge and without sufficient public oversight. In close collaboration with our team the candidate will implement and critically evaluate a range of machine vision approaches to reveal how machine vision leverages and operates on public user data; ultimately improving our understanding of machine vision systems and their role in society.