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PhD in Longitudinal Natural Language Processing Methods with Applications to Mental Health

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  • Full or part time
    Prof M Liakata
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

We are advertising a PhD position in the context of the Alan Turing Institute AI fellowship on “Creating time sensitive sensors from user-generated language and heterogeneous content”, led by Prof Maria Liakata. The Alan Turing Institute is the UK’s national Institute for data science and artificial intelligence. Prof Liakata’s AI fellowship is one of five prestigious such awards made in Autumn 2019 and funded by the UK Department of Business, Energy and Industrial Strategy (BEIS). Her fellowship aims to strengthen the nascent area of personalised longitudinal natural language processing (NLP) models from user-generated content (UGC). More information about the AI fellowship is available at: https://www.turing.ac.uk/people/researchers/ai-fellows

Overview
The successful applicant will join Prof Liakata’s research team for a four-year PhD degree at the School of Electronic Engineering and Computer Science (EECS) at Queen Mary University of London (UK) and The Alan Turing Institute (London, UK). EECS is an exciting and dynamic environment with a reputation for excellence in both research and teaching. We are 11th in the UK for quality of computer science research (REF 2014) and 6th in the UK for quality of electronic engineering research (REF 2014). Our academics undertake world-leading research in a lively and supportive research community.

The PhD project will focus on the intersection of natural language processing (NLP), machine learning (ML) and mental health. The overall goal is to combine UGC from social media and smart devices in a longitudinal fashion, aiming at representing the user in a temporally sensitive way, with particular focus on capturing his/her mental health state over time. The successful applicant is expected to work on two or more of the following:
• Longitudinal dynamic representations of language and other UGC;
• Multi-scale methods for combining user-generated heterogeneous data at different temporal granularities;
• Methods for synthetic language generation from language and heterogeneous UGC;
• Methods for change point detection in language use and behaviour;
• Longitudinal predictions for condition change;
• An appropriate evaluation framework for each task;
• Methods for combining longitudinal evidence for condition change into interpretable summaries;
• Co-design of new instruments for assessing changes in mental health based on interpretable summaries and longitudinal patterns of change in heterogeneous language and UGC.
• Modeling ethical aspects of longitudinal language sensors.

Desired outputs include publications in top-tier NLP and ML venues and are expected to have high impact in the fields of NLP and mental health, both in terms of methodological innovation and their application to better understanding real-world mental health data. There will also be the opportunity to work closely with research engineers at the Alan Turing Institute and contribute to the creation of language sensors (software tools and libraries that incorporate personalised longitudinal language modeling methods).

Desired Background
Minimum Qualifications:
• Academic degrees: [MSc or First-Class BSc] in [Natural Language Processing, Computer Science, Machine Learning, Artificial Intelligence, Engineering or related field]. Exceptional candidates with a background in Psychology or Psychiatry may also be considered.
• Experience in at least one of the areas below: Natural Language Processing/Machine Learning/Deep Learning
• Programming Skills: Strong coding skills, preferably in Python.

Desired Qualifications:
• Programming skills: Strong experience with the following python modules: numpy, pandas, sklearn, scipy, matplotlib;
- Experience with git;
- Experience with tensorflow and/or pytorch
• Research Experience: Experience with working in research projects
- Working with UGC, such as text published in social media and/or digital traces from smart devices
- Publication(s) in NLP/ML venues
• Interest in/ Knowledge of Mental Health applications & associated challenges

Application Information, Studentship and Eligibility
Studentship and Eligibility:
• Full time PhD tuition fees for someone with UK/EU nationality (est. at £13,542.27 overall).
• An annual (tax free) stipend of £20,500 per year, for 3.5 years.

Application Process:
Applicants are invited to submit their applications online at: https://www.qmul.ac.uk/postgraduate/research/applying-for-a-phd/. Please submit the following:
• BSc/Diploma/PG Degree transcripts (translated in English, if needed)
• CV (max 2 pages)
• Cover letter (max 4,500 characters)
• Research proposal (max 500 words)
• 2 References
• Certificate of English Language (for non-native students)
• Other (academic or not) Certificates

For queries regarding the PhD topic and project, please contact Prof Maria Liakata ([Email Address Removed]). For queries about the application process, refer to [Email Address Removed] .

Deadline for Applications: 9th May 2020.
Interview are expected to take place week commencing 25th May 2020.


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