AI at Work: A Hybrid Study of Artificial Intelligence and Machine Learning Research
|AI at work|
|Dates||October 2019 - 2023/10/01|
|Address||University of liverpool|
|Geolocation||53° 24' 21", -2° 57' 56"|
|Final version due|
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AI at Work: A Hybrid Study of Artificial Intelligence and Machine Learning Research:
AI at Work: A Hybrid Study of Artificial Intelligence and Machine Learning Research CASE 1+3 Studentship (Masters and PhD)
University of Liverpool
Economic and Social Research Council (ESRC) North West Social Science Doctoral Training Partnership (NWSSDTP)
The Department of Sociology, Social Policy and Criminology at the University of Liverpool invites applications for this full-time 1+3 studentship funded by UK Research and Innovation (UKRI) through the North West Social Science Doctoral Training Partnership (NWSSDTP) via its 2018 Artificial Intelligence (AI) call. The studentship is part of a collaboration with Peak AI (one of three companies to be Global Amazon Web Services Accredited Machine Learning Partners) and the Big Hypotheses Project (one of five large projects funded in response to a UKRI call on New Approaches to Data Science, which is led by the University of Liverpool and also involves UKRI’s Hartree Centre (a UK centre of excellence for supercomputing) and IBM Research).
This unique studentship is open to candidates with either a social science or data science background. Candidates will be expected to have or be on track for a 1st or strong 2:1 BA/BSc degree in a relevant social science (e.g., anthropology, geography, politics, psychology, sociology, science and technology studies) discipline or in mathematics, statistics, data science or computer science at undergraduate level. However, the studentship is also open to those who have already completed MA/MSc degrees in a relevant social science discipline, in statistics/mathematics or in data science and who are interested in additional training that will enable them to pursue new trajectories of research in cutting edge AI/Machine Learning research fields. This is possible because the proposed ‘hybrid’ project will offer the successful candidate two six-month placements in high profile AI and Machine Learning projects during which they will analyse those projects sociologically and ethnographically – asking how AI and Machine Learning work actually gets done and what is involved in doing it. Given this, candidates should ideally (a) have some experience/interest in social studies of science and technology (see, e.g., Sormani 2014, Vertesi 2015 and Mackenzie 2017) and (b) have undertaken or be prepared to undertake specialist training in ethnomethodology and conversation analysis, and/or data science as part of their training.
This four-year studentship will commence October 2019. The aim of the proposed studentship is to provide a bridge between, on the one hand, work in AI and Machine Learning and, on the other, the social sciences. It will do this through what has been termed (by the sociologist Harold Garfinkel (2002)) a ‘hybrid study’. This hybrid study will involve embedding the successful candidate – a social scientist trained in data science or a data scientist trained in social research – in two AI/Machine Learning work settings and asking them to study those settings by participating in the work conducted there. These embedded studies, as part of which the successful candidate will become a ‘hybrid practitioner’ comfortable in both research worlds, will generate new insights into the practical operations of AI and Machine Learning research for non-AI or Machine Learning audiences and, through that: help deliver better public understandings of these significant but frequently misunderstood contemporary technologies; sharpen understandings of their potential uses in different applied settings; and contribute to developing the effective training that will be needed to bring forward the hybrid researchers of the future, i.e. the growing number of those who will be operating across computer science, social science and arts and humanities domains as a matter of course. All this will be achieved through a focus on the practicalities of the work – on what AI and Machine Learning actually involve as practices.
The successful candidate will be registered at the University of Liverpool and will undertake research on AI and Machine Learning from bases in Liverpool, Manchester and Lausanne at different periods in the research. The student will be supervised by Dr Michael Mair (Sociology, University of Liverpool), Dr Phillip Brooker (Sociology, University of Liverpool), Dr Philippe Sormani (STS-Lab, University of Lausanne), Dr Will Dutton (Peak AI, Manchester) and Prof Simon Maskell (Principal Investigator for Big Hypotheses, University of Liverpool).
Application process and general information :
To apply please submit:
- An up-to-date CV including details of two named referees (one of whom should be your most recent academic tutor/supervisor)
- A letter of application (not exceeding 2 pages) outlining your interest in, and suitability for, the studentship and how you would anticipate approaching the research
- Copies/confirmation of your University qualifications
Applications – with ‘UKRI 1+3 AI Award: AI at Work’ in the subject line of the email should be submitted by the 23/11/2018 5pm to: Mrs Leah Dempsey – slsjpgr @ liverpool.ac.uk
- Dr Michael Mair
- Dr Phillip Brooker
- Professor Simon Maskell
- Dr Philippe Sormani