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PhD studentship on NLP Approaches to Mental Health Research (Fixed Term)

Fixed-term: The funds for this post are available for 3 years.

This PhD project will explore the use of NLP for mental health research, with the following aims:

  1. To develop multimodal, explainable machine learning approaches to predict relapse for individual patients with early stage psychotic disorders, based on speech data.

  2. To use NLP tools to analyse recovery stories from people with lived experience of mental health conditions, to understand how people tell their stories and which features of recovery stories might be helpful for people with current mental health problems. This work is in collaboration with Prof Mike Slade, Dr Stefan Rennick-Egglestone and colleagues at Nottingham University (

  3. There may also be some scope to investigate the neural underpinnings of altered language use in mental health conditions using brain MRI data, depending on the applicant's interests.

Mental health conditions are some of the most debilitating health conditions, and one of the main causes of the overall disease burden worldwide (Vos et al, 2013). In England, it is estimated that 1 in 6 people in the past week experienced a mental health problem (McManus et al, 2016).

Recent research suggests that NLP markers of transcribed speech might be powerful predictors of individual disease trajectories for patients with early stage psychotic disorders (Corcoran et al 2018, Mota et al 2018, Morgan et al 2021). A number of questions remain however, including whether similar approaches can be used to predict relapse for patients tapering off medication and how best to design algorithms that are explainable and combine relevant features of speech. There are also exciting opportunities to use NLP measures to study recovery stories from people with lived experience of mental health conditions, to understand how people tell their stories and the ways in which sharing recovery stories might help others with ongoing mental health conditions.

The student will work closely with people with lived experience of mental health conditions, clinicians and other mental health research professionals, drawing on technical expertise in machine learning, NLP and network science to deliver better health outcomes. The student will therefore be co-supervised by researchers at the Department of Computer Science and Technology (Dr Sarah Morgan) and the Psychiatry Department (Dr Graham Murray, Dr Hisham Ziauddeen and Dr Petra VĂ©rtes). They will also benefit from being part of the new Accelerate Science community at the Department of Computer Science and Technology, which brings together researchers aiming to accelerate the use of machine learning in scientific domains.

Requirements: The successful applicant should have at least a good upper-second class honour's degree in a relevant subject, a non-exhaustive list of subjects includes: Computer Science, Physics, Maths, Engineering, Psychiatry, Psychology, Neuroscience or similar. We welcome applicants from a wide range of backgrounds.

Funding: This PhD studentship is funded by the WD Armstrong Trust Fund (W.D Armstrong Trust Fund ' School of Technology ( The financial support comprises approved College and University fees (at the Home rate) and maintenance paid in accordance with the standard EPSRC yearly rate, for 3 years.

To apply, in the first instance please send a CV, academic transcripts and a cover letter explaining why you are interested in the position to Dr Sarah Morgan at by 31 August 2022. You are also welcome to email Dr Morgan with any questions prior to applying. Interviews will be held at the beginning of September. Following this process the chosen candidate will be asked to make a formal application via University Application Portal from which candidates should select the Department or Institute to which they wish to apply. At this stage, applicants are also advised to consult the University Graduate Admissions Website for details of the admissions process.

Please quote reference NR32379 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.