Don't miss our weekly PhD newsletter | Sign up now Don't miss our weekly PhD newsletter | Sign up now

  PhD Studentship in Detection and Mitigation of Online Harms


   School of Electronic Engineering and Computer Science

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 A Zubiaga  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Applications are invited for a full PhD Scholarship starting in January 2020 (or as soon as possible thereafter) to undertake research in the areas of social media mining, natural language processing and computational social science, with a focus on tackling online harms such as disinformation and hate speech in social media. The project aims to develop novel methods that assist with the detection of deceitful and harmful content online, especially where they can have a damaging effect on individuals or society at large, or an impact offline leading to crime. The PhD will be supervised by Dr. Arkaitz Zubiaga (http://www.zubiaga.org/).

The PhD will be based in the QMUL Cognitive Science (CogSci) Research Group (http://cogsci.eecs.qmul.ac.uk/), an interdisciplinary group with strong publication record and high international impact, which is part of the School of Electronic Engineering and Computer Science (http://www.eecs.qmul.ac.uk), Queen Mary University of London, UK.

Qualifications:
All applicants should have a first-class honour degree or equivalent, or a MSc degree, in Computer Science (or a related discipline). Applicants should have a good knowledge of English and an ability to express themselves clearly in both written and spoken form. The successful candidate must be strongly motivated to undertake doctoral studies, must have demonstrated the ability to work independently and to perform critical analysis.

Applicants are expected to possess fundamental knowledge and skills in two or more of the following aspects:
• Excellent knowledge of data science methods.
• Prior experience in social media mining and/or natural language processing.
• Excellent programming skills, ideally in Python.
• Experience with deep learning algorithms.
• Experience working with large datasets.

All nationalities are eligible to apply for this studentship. We offer a 3-year fully funded PhD studentship supported by Queen Mary University of London including student fees and a tax-free stipend starting at £16,777 per annum. In addition to the studentship, we also welcome applications from self-funded students with relevant backgrounds.

To apply, please follow the online instructions specified by the college website for research degrees: http://www.eecs.qmul.ac.uk/phd/how-to-apply/. Steps 2 onwards are applicable in this case. Please note that we request a ‘Statement of Research Interests’. Your statement (no more than 500 words) should answer two questions:
(i) Why are you interested in the topic described above?
(ii) What relevant experience do you have?

In addition to this, we would also like you to submit a sample of your written work. This might be a chapter of your final year or masters dissertation, or a published conference or journal paper.

In order to submit your online application you will need to visit the following webpage: https://www.qmul.ac.uk/postgraduate/research/subjects/computer-science.html. Please scroll down the page and click on “PhD Full-time Computer Science - Semester 2 (January Start)”. The successful PhD candidate will be a member of the CogSci research group. You should mention this in your application.

Applicants interested in the post, seeking further information or feedback on their suitability are encouraged to contact Dr. Arkaitz Zubiaga at [Email Address Removed] with subject “Online Harms PhD Studentship”. All applications must be made via the website mentioned above.

The closing date for applications is 8th September, 2019.
Interviews are expected to take place in September 2019.
Starting date: January 2020 (dates can be flexible).


 About the Project