The University of Edinburgh, in partnership with AHRC, SGSAH and Microsoft Research (UK) are offering a 3.5-year full-time / 7 year part-time funded Collaborative Doctoral Award.
Qualification: PhD (School of Design)
Location: Edinburgh
Eligibility: Applications are open both home fee status (including EU nationals with pre/settled status who meet residency requirements) as well as international students. Students who do not require UKVI sponsorship via a student visa may apply to study their PhD on a part time basis.
Duration: 3 years and six months, Full time / 7 years part time (September 2023 start)
Award: The successful applicant will receive an annual stipend at the UKRI rate (£18,622 for 23/24) for 3 years 6 months full-time (pro-rata part-time), plus PhD tuition fees. The award also provides an individual research training support grant (RTSG) of £1750 to support travel to conferences.
Background
Generative AI (GAI) - a type of AI able to autonomously create new content from data - threatens to transform the field of knowledge work.
Knowledge work involves the production, diffusion and application of knowledge, with an emphasis on creativity and individual learning, and was (until recently) considered a form of work least threatened by technology. However, the emergence of generative AI has challenged this view. Suddenly creative practitioners, lawyers, academics and authors (to name but a few) are wondering what the future of their profession holds.
User-friendly, readily available GAI tools have dramatically altered the landscape leading to AI-generated ‘art’, and written work that can trick plagiarism tests. This latest tranche of systems requires no expert operational knowledge, generating dramatic results from simple prompts.
Such technologies hold the potential to alter human experience in profound ways but, without careful consideration, could lead to unintended consequences with far-reaching effects. Whilst concerns over data bias, algorithmic opacity, and attending harms are now well-established fields of investigation, the implications of GAI for knowledge workers have only now begun to raise concerns.
The Project
This project will explore the potential implications of generative AI for the future of knowledge work in the digital economy. Specifically, (a) within which areas of knowledge work has GAI had most impact, and how? (b) what future socio-moral issues are likely to arise for knowledge workers and their organisations, and how might such issues be mitigated? (c) how might human-GAI partnerships be managed, through design? (e.g., considerations of workflow, teaming, distribution of cognition).
The successful applicant will be expected to tailor the methodology to their research questions and skillset.
The project has been awarded funding by the Scottish Graduate School for Arts and Humanities (SGSAH). The PhD will be based in the School of Design at Edinburgh College of Art in the University of Edinburgh. The student will be supervised by Prof Ewa Luger in the Institute of Design Informatics, School of Design, and Dr Morgan Currie from the School of Social and Political Science, in addition to supervision from Dr Siân Lindley at Microsoft Research in Cambridge (UK). The student may have the opportunity to be based at a Microsoft research site for a proportion of the studentship.
The Person
This is a multidisciplinary research area, and we are keen to receive applications from people with a wide range of experience and from across the academic disciplines. For example, you might come from a politics or social science background and be keen to understand how to develop and evaluate prototype example of your ideas. Alternatively, perhaps your academic qualifications are from the computer sciences or Design, but you wish to engage more with theory or social science. Candidates from entirely different academic disciplines are also welcome, particularly if they have relevant professional experience.
Regardless of previous specialism, the successful applicant for the studentship should (by September 2023) have a First or high Upper-Second class honours degree and, preferably, a Masters-level qualification or comparable professional experience. A key aim of this studentship is to help develop a future research leader in this important domain. As such, candidates should expect to learn new, cross-disciplinary skills during the project, including those related to empirical study design, data science/analysis and cutting-edge technology.
Prior experience and key attributes
Essential:
Desirable:
Applicants will need to meet the requirements of postgraduate studies at the university, and will also apply through the University of Edinburgh’s online application system.
How to Apply
Apply for the PhD Design (Full-Time) through the University of Edinburgh’s online postgraduate application system by no later than Thursday 8th June 2023.
Applications should be accompanied by:
a) A CV, certified copies of degree certificates and transcripts, the details of two referees and proof of eligibility (eg. a copy of your passport or residence permit)
b) A short example of writing (eg. chapter, paper, blog or article)
c) A statement/proposal, tailored to the call, of no more than two pages describing:
IMPORTANT: please also send an email to the ECA Postgraduate Research team: ecaresearchdegrees@ed.ac.uk stating that you wish to be considered for the “Designing Knowledge Futures” AHRC/SGSAH PhD studentship, and noting that you have made an online application. This allows us to fast track your application on the online postgraduate application system.
For queries about the application process
Please contact ecaresearchdegrees@ed.ac.uk
For further Information about the PhD
Please contact Prof Ewa Luger (ewa.luger@ed.ac.uk) or Dr Siân Lindley (sianl@microsoft.com).
Timetable
Qualification Type: | PhD |
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Location: | Edinburgh |
Funding for: | UK Students, EU Students, International Students |
Funding amount: | £18,662 for 23/24 |
Hours: | Full Time, Part Time |
Placed On: | 26th May 2023 |
Closes: | 8th June 2023 |
Type / Role:
Subject Area(s):
Location(s):
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