Socio-Technical Futures Lab – Machine Vision


Emerging technologies such as machine vision, AR/VR, AI and recommendation systems raise profound questions about the relationship between technology and society, and how these technologies are becoming differentially integrated into everyday life in Australia and elsewhere. Yet the invention, design, implementation, and use of technology proceeds without such knowledge. 

The Socio-Tech Futures Lab (STuF Lab) has been established in the Department of Media and Communication to examine the ways in which social, cultural, and political dynamics influence the integration of technologies into everyday life, and the implications of such forces in shaping and designing our futures. See - STuF Lab 

Led by Professor Heather Horst, Professor Gerard Goggin and Dr Marcus Carter, the STuF Lab is seeking PhD students interested in bringing humanities and social science research to the table with other disciplines, community, industry and policy actors in the study of emerging digital technologies.   

The exemplary PhD candidate is not expected to have pre-existing practical or high-level technical literacy about these emerging technologies. They will be supervised by a multidisciplinary team of senior researchers, incorporating the knowledge, industry experience and technical expertise available at The University of Sydney in their chosen research area. 

We are currently recruiting PhD students interested in conducting projects on AI, Machine Vision, AR/VR, and on Recommendation Systems


Professor Gerard Goggin, Dr Marcus Carter

Research Location

Department of Media and Communications, School of Letters, Art and Media (SLAM)

Program Type



Machine Vision refers to the interface between computers and the world around them, the manner in which robots, autonomous cars and surveillance systems register and make sense of their environment. While significant media attention has been given to issues such as AI-based decision making of autonomous cars (e.g. see the 'What can the trolley problem teach self-driving car engineers?' Marshall, A. (2018)), the assumption in these debates is often perfect machine vision; a computer system that fully registers and understands its environment. This is invariably not the case. The ways in which machine vision systems do and do not register their environment, misrecognise uncommon configurations and shapes, and are limited by the technologies and techniques that they rely on will introduce inequities and unfairness to their design, use and adoption ('AI can be sexist and racist—it’s time to make it fair' Zou, J., & Schiebinger, L. (2018); 'Who’s Afraid of Amazon’s Video Doorbell?' Misra, T. (2018)). 

The specific nature of this project will be developed in consultation with the prospective PhD student and identified supervisor(s). On these topics, you will be working alongside other researchers and post-doctoral researchers interested in these fields of enquiries and advancing knowledge in your own right. Prospective candidates are not required to have advanced technical literacy in their chosen topic area, although - depending upon the project - an openness to learning these is always welcome.

Additional Information

  • This is not a funded position, although opportunities for research assistance work and travel funding may be available through association with the STuF Lab. The applicant is responsible for obtaining a stipend.  
  • Initial Inquiries should be sent by email to Professor Heather Horst and should be include a copy of the applicant’s CV and a 500 – 1,000 word project proposal on one of the suggested topics. 
  • Depending on the research project proposal, we will connect applicants to potential supervisors from the Department of Media and Communications and across the University of Sydney.

Want to find out more?

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machine vision, autonomous cars, surveillance systems, roboticists, AI, artificial intelligence, emerging digital technologies, technology and society, Socio-Tech Futures Lab, STuF Lab, Department of Media and Communication, future

Opportunity ID

The opportunity ID for this research opportunity is: 2624

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