|Funding for:||UK Students, EU Students|
|Funding amount:||A tax-free stipend of £15,285 per annum|
|Placed On:||21st December 2021|
|Closes:||31st January 2022|
Project: Society-at-Large Inference (SALI)
Project reference: SALI-PhD
This 36 month (3 year) fully-funded PhD Studentship, in-line with the Research Council values, comprises a tax-free stipend of £15,285 per annum (paid monthly) and a home fees studentship (£4,500 for 2021-22) for up to 3 years. The bursary is renewable annually for up to 36 months in total, subject to you making satisfactory progression within your PhD research.
This opportunity is open to UK and EU applicants with a pre/settled status. All applicants will receive the same stipend irrespective of fee status.
How to Apply
To apply, please complete the project proposal form, ensuring that you quote the project reference, and then complete the online application where you will be required to upload your proposal in place of a personal statement as a pdf document.
You will also be required to upload two references, at least one being an academic reference, and your academic qualification/s.
To discuss the application process please contact DRC.CEBE@bcu.ac.uk.
The deadline for Home and Pre/Settled applicants to apply is 23:59 on 31st January 2022.
Topics of Interest
Machine Learning in Mobile App Environment, Edge Computing, Graph Theory, Probabilistic Graphical Modelling, ML Ops and Federated Learning.
The connected digital world owes a significant debt to mobile communication through smartphones. The widespread use of social media has been met with a surge in streaming services. Consumers of digital media in these forms who would traditionally have been disjoint, now share commonalities across traditional boundaries. Historically, media outlets and broadcasters controlled these communications and set trends and themes nationwide. However, research has shown that trends have transcended beyond the media and new issues have arisen. Individuals acting on these networks can be treated as nodes on a graph, where the linkages between nodes form a ‘Pattern of Life’.
The challenge is to identify latent structures in the connected network. Latent variable modelling is typically applied to static high-dimensional structured or dynamic low-dimensional domains. The connections here are naturally stochastic requiring the bridging of Graph Theory and Probabilistic Graphical Modelling to characterise themes which form paths. The future of themes, trends and network linkages is subject to significant levels of uncertainty. The further stages of the project will extend the algorithms into a dynamic framework to manage temporal evolutions. This framework will facilitate queries and inference allowing users to identify emerging trends and adaptively react; leading to structured, targeted marketing and broadcasting content.
PhD Applicant Profile
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