or
Continue with FacebookLooking to list your PhD opportunities? Log in here.
This project is no longer listed on FindAPhD.com and may not be available.
Click here to search FindAPhD.com for PhD studentship opportunities
Dr M Mair
Dr P Brooker
No more applications being accepted
Competition Funded PhD Project (Students Worldwide)
About the Project
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.
The studentship/project/supervision
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 – [Email Address Removed]
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.
The studentship/project/supervision
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 – [Email Address Removed]
Funding Notes
Tuition fees paid at Home/EU/International Level
Annual stipend of £14,777
Annual stipend of £14,777
References
Two referees
Project supervisors
Career overview
Professor Michael Mair completed an undergraduate degree in Politics and Philosophy at the University of Edinburgh before pursuing MSc and PhD studies at the University of Manchester, where he worked with Wes Sharrock. He joined the Sociology, Social Policy and Criminology department at the University of Liverpool in 2010 and currently holds the position of Professor of Sociology. His primary research interests encompass politics, the state, and governmental practice, particularly focusing on their evolving contemporary forms and associated accountability issues. Professor Mair''s empirical research is largely sociological, ethnographic, and practice-oriented, exploring new governance methods from an ethnomethodological perspective. He is currently investigating how politics is practised within new bureaucratic and administrative structures and addressing various accountability challenges in different contexts, including armed conflict settings. Additionally, he has a general interest in the methodology and philosophy of both the natural and social sciences, integrating empirical studies involving qualitative, quantitative, and digital methods, as well as experimentation, machine learning, and artificial intelligence. Alongside his research and teaching responsibilities, Professor Mair serves as the Director of engage@liverpool, a cross-faculty initiative focused on research methods and methodology, is an Executive Board Member of Methods North West, and is a Senior Fellow at the National Centre for Research Methods (NCRM), having led the University to become an NCRM Centre Partner in 2020.
Research interests
Professor Mair''s primary research interests lie in politics, the state, and governmental practice, particularly their changing contemporary forms and the associated problems of accountability. He investigates new ways of governing from an ethnomethodological perspective, focusing on how politics is practised and realised through new bureaucratic and administrative structures. His research also addresses various issues related to accountability in diverse settings, including armed conflict. Additionally, Professor Mair has a general interest in the methodology and philosophy of the natural and social sciences, which encompasses empirical studies of qualitative, quantitative, and digital methods, as well as experimentation, machine learning, and artificial intelligence. He is involved in a cross-faculty research methods and methodology initiative at the University of Liverpool and serves as an Executive Board Member of Methods North West, contributing to regional collaborations.
Sociology
Political Science
Governmental Practice
Accountability
Ethnomethodology
Bureaucratic Structures
Administrative Structures
Qualitative Methods
Quantitative Methods
Digital Methods
Experimentation
Machine Learning
Artificial Intelligence
Research Methodology
Social Sciences
Natural Sciences
Ethnographic Research
Practice-Oriented Research
Career overview
Dr Phillip Brooker is a Senior Lecturer in Sociology at the University of Liverpool, part of the School of Law and Social Justice within the Faculty of Humanities and Social Sciences. His research and teaching span a range of interdisciplinary fields, including ethnomethodology, conversation analysis, computer-supported cooperative work, human-computer interaction, the philosophy and sociology of science, science and technology studies, and digital social research methods. Dr Brooker''s academic journey began with a video-aided ethnomethodological study focusing on the use of programming languages in astrophysics and electrical engineering for his thesis. Following this, he worked as a postdoctoral researcher, contributing to the development of a software package called Chorus, which facilitates social science research involving Twitter data. His work has led to innovative methods for social media analytics, addressing issues such as poverty porn, class stigma, weight stigma, far-right politics, and fake news. Dr Brooker has also been involved in developing research and teaching around the integration of computer programming into core social science research methods training. He has conducted various research projects and teaching activities, including ''Bootcamps'' through the National Centre for Research Methods and authored the book ''Programming with Python for Social Scientists.'' His current research interests include the application of physical hardware maker skills, such as 3D printing and electronic engineering, to social science research. He is particularly focused on ethnomethodological approaches to human spaceflight missions, studying NASA''s Apollo 13 mission and the Skylab program using publicly available materials. Dr Brooker is also developing new projects, ''Skylab 2049'' and ''Terra Pi,'' which aim to engage with astronautics through programming skills and game design.
Research interests
Dr Brooker''s research is grounded in sociology but spans various interdisciplinary fields, including ethnomethodology, conversation analysis, computer-supported cooperative work (CSCW), and human-computer interaction (HCI). He focuses on the philosophy and sociology of science and knowledge, particularly ordinary language philosophy, as well as science and technology studies (STS), with specific interests in the social studies of human spaceflight and artificial intelligence. His work is unified by a focus on social and collaborative computing and the development and usage of software. Dr Brooker''s thesis was a video-aided ethnomethodological study examining the use of programming languages in astrophysics and electrical engineering. As a postdoctoral researcher, he contributed to the development of a software package for social science research involving Twitter data, known as Chorus, which has facilitated innovative methods for social media analytics. Dr Brooker has explored various topics, including the cultures of fear and the stigmatisation of social groups, and has developed critical software interventions addressing issues such as poverty porn, class stigma, weight stigma, far-right politics, and fake news. He has also integrated computer programming into social science research methods training, evident in his teaching activities and his book ''Programming with Python for Social Scientists.'' Currently, he is researching the potential of physical hardware maker skills, such as 3D printing and electronic engineering, to enhance social science research. His ongoing research includes bringing ethnomethodological approaches to the study of human spaceflight missions, focusing on NASA''s Apollo 13 mission and the Skylab program, utilising publicly available legacy materials. He is also developing projects titled ''Skylab 2049'' and ''Terra Pi,'' which aim to engage with the lived work of astronautics through programming skills and game design.
Sociology
Ethnomethodology
Conversation Analysis
Computer-Supported Cooperative Work
Human-Computer Interaction
Philosophy of Science
Sociology of Knowledge
Science and Technology Studies
Social Studies of Human Spaceflight
Artificial Intelligence
Digital Social Research Methods
Social Media Analytics
Programming-as-Social-Science
Maker Skills
3D Printing
Electronic Engineering
Social Computing
Software Development
Critical Software Interventions
Poverty Porn
Class Stigma
Weight Stigma
Far-Right Politics
Fake News
Astronautics
NASA Apollo 13
Skylab
Ethnomethodological Study
Programming Skills
Game Design
Electronics Maker Skills.
Find a scholarship to fund your dream Masters
Sign in to view and filter all scholarship opportunities
or
Continue with Facebook