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Using Machine Learning and Complex Networks to Model Scientific Echo Chambers, Computer Science – PhD (Funded)

About This PhD Project

Project Description

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences, is inviting applications for a fully-funded PhD studentship to commence in September 2019 or as soon as possible thereafter. For eligible students the studentship will cover tuition fees plus an annual tax-free stipend of at least £15,009 for 3.5 years full-time, or pro rata for part-time study. The student would be based in Computer Science in the College of Engineering, Mathematics and Physical Sciences at the Streatham Campus in Exeter.

Location: Computer Science, Streatham Campus, Exeter

Project Description:
Web personalisation is currently common place. Most sites recommend friends, products, and more pervasively websites. It is well known that personalisation may lead to the creation of segregated communities what is popular known in social sciences as echo chambers. The name alludes to the fact that when communities are segregated, the information that flows within such communities are echoed and amplified from within the group, making members of the group blind or incapable of noticing new information about a particular topic. Such phenomenon has been observed in many topical areas such as: politics, climate change, vaccination, etc. Yet, people has not yet tried to quantify how personalization affect the scientific community, meaning that researchers rely heavily on search engines for finding published relevant to their area of interest. Given that personalisation uses a multitude of attributes such as language, geographical location, social connections, to name a few, it stems naturally that personalization may be driving how scientists use citations in their work and which works they can actually see. The existence of echo chambers in science could be disastrous and lead to dire consequences. This project aims at looking for possible signs of echo chambers in science as a consequence of personalisation, coupled with the introduction of measures to minimize their effects. The selected student will be working with Artificial Intelligence, Machine Learning, Social Network Modelling, and Complex Network Analysis.
This award provides annual funding to cover tuition fees in full and a tax-free stipend of at least £15,009 per year tax-free stipend.
The studentship will be awarded on the basis of merit for 3.5 years of full-time study to commence in September 2019.

Entry requirements
Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology.

If English is not your first language you will need to have achieved at least 6.0 IELTS and no less than 6.0 in any section by the start of the project. Alternative tests may be acceptable (see

How to apply
Apply now

In the application process you will be asked to upload several documents:
• CV
• Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).
• Research statement outlining how your background fits to the project description.
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
• Names of two referees familiar with your academic work. You are not required to obtain references yourself. We will request references directly from your referees if you are shortlisted.
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English

The closing date for applications is midnight on 15 April 2019. Interviews will be held on the University of Exeter Streatham the week commencing 1 May 2019.
If you have any general enquiries about the application process please email or phone +44 (0)1392 722730. Project-specific queries should be directed to the main supervisor.

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