Fully funded PhD Studentship in AI and Journalism
We seek applications from excellent, enthusiastic and self-motivated students for three-year funded PhD studentships in Surrey Institute for People-Centred Artificial Intelligence (AI) at the University of Surrey.
Funded doctoral student will research in the intersection of AI and Journalism, and will specifically contribute towards a project with the BBC on ‘Responsible AI Literacy in Journalism’. In addition to excellent knowledge of AI and data-driven methods, multidisciplinary background in media and communication, or other relevant social science disciplines would be necessary.
Start date
2 January 2025Duration
Up to 3.5 yearsApplication deadline
Funding source
Institute for People-Centred AIFunding information
UKRI standard studentship
Stipend: £1,600 per month (£19,237 per year). This is tax free.
Fees: UK home rate fees. In exceptional cases, we may be able to waive the non-UK fees for an exceptional international/European candidate.
Research, training and support: £3,000 over the project.
Supervised by
About
Fundamental to well-informed and democratic societies, journalism relies heavily on trust, credibility, and accountability. While AI offers enhanced capabilities and efficiency, there’s a risk it may amplify biases and disinformation. Such pitfalls could lead to biased or inaccurate reporting, misinforming the public and exacerbating societal divides. Hence, in journalism committing to the highest standards of responsible AI is critical. This commitment means ensuring accuracy, fairness, and public trust, while fostering accountability and transparency in reporting. This project seeks to enhance responsible AI adoption in the newsroom, and promoting cross-team and interdisciplinary communication and coordination within the news organisations.
The student will work closely with the supervisor on a project in partnership with the BBC on ‘Responsible AI Literacy in Journalism’. Through this, the student is expected to engage with BRAID (Bridging the Responsible AI Divide) programme, which a UK-wide programme dedicated to integrating Arts and Humanities research more fully into the Responsible AI ecosystem, as well as bridging the divides between academic, industry, policy and regulatory work on responsible AI.
Eligibility criteria
Open to any UK or international candidates.
All applicants must have (or expect to obtain) a first-class degree or an MSc/MA in Information Science, AI, Sociology, Journalism, Communication and Media or a relevant discipline, and a strong interest and demonstrable knowledge in pursuing research in this field.
Candidates with Masters degrees are preferred. Candidates with a multidisciplinary educational background, consisting of a mix of social sciences/journalism, communication and media/humanities/arts AND information/computer science degrees are particularly encouraged to apply.
In addition to excellent knowledge of AI and data-driven methods, multidisciplinary background in media and communication, or other relevant social science disciplines would be advantageous.
The candidate must have a good understand of qualitative and quantitative research methods and their application. A track record of publications and/or presentation in journals and conferences is highly valued. Experience in working in me the media industry and/or expertise in other forms of communication is also appreciated.
We seek applicants who are self-motivated, enthusiastic and proactive, can work independently, collaborate effectively, and contribute towards a richer environment for themselves and their research community.
How to apply
Applications should be submitted via the Vision, Speech and Signal Processing PhD programme page. In place of a research proposal you should upload a document stating the title of the project that you wish to apply for and the name of the relevant supervisor.
We review applications on a rolling basis and encourage early submissions. The recruitment process will continue until a suitable candidate is found, at which point the position may close prior to the deadline.
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