University of Wisconsin–Madison

Job Announcement: UKRI 1+3 AI Award: AI at Work

AI at Work: A Hybrid Study of Artificial Intelligence and Machine Learning ResearchCASE 1+3 Studentship (Masters and PhD)

Project Description: 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.

More information: https://www.findaphd.com/search/ProjectDetails.aspx?PJID=100716