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Predicting cognitive performance in ADHD with advanced deep learning and network analysis in functional neuroimaging (Deakin led)

Eligibility: UK/International (including EU) graduates with the required entry requirements

Duration: Full-Time – between three and three and a half years fixed term

Application deadline: 15 July 2024

Interview date: Will be confirmed to shortlisted candidates

Start date: January 2025

For further details contact: Dr Fei He


This is an exciting opportunity to study a PhD as part of a cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia. The PhD Student will graduate with two PhDs, one from Deakin University and one from Coventry University, each of which recognises that the program was carried out as part of a jointly supervised doctoral program.

The program is for a duration of 3.5 years (funding only for 3.5 years, maximum allowed time 4 years) and scheduled to commence in January 2025. 

Project details

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity and or impulsivity. The diagnosis is based on subjective report, and as yet there are no objective biomarkers used clinically. In addition to the core symptoms, individuals with ADHD often present with a profile of cognitive deficits, that has been argued to results from lapse of attention. Being able to identify the brain networks and neural signature of an attentional lapse, will not only further our understanding of the neurobiology, but could also be used to monitor and predict cognitive performance, offering potential target for intervention.  

This project aims to explore whether EEG and/or resting state fMRI can provide a quantitative and effective approach to identify a neural signature of attention lapses during cognitive task. Additionally, we would like to evaluate whether an integration of network-level analysis and deep learning techniques would improve the prediction compared with traditional methods.


Tuition fees and bursary


The successful candidate will receive comprehensive research training including technical, personal and professional skills.

All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities. 

Candidate specification

Applicants must meet the admission and scholarship criteria for both Coventry University and Deakin University for entry to the cotutelle programme.

  • Applicants should have graduated within the top 15% of their undergraduate cohort. This might include a high 2:1 in a relevant discipline/subject area with a minimum 70% mark (80% for Australian graduates) in the project element or equivalent with a minimum 70% overall module average (80% for Australian graduates).
  •  A Bachelor's degree in a relevant field requiring at least four years of full-time study, and which normally includes a research component which is equivalent to at least 25% of a year’s full-time study in the fourth year, with achievement of a grade for the project equivalent to a H1 standard or 80%


  •  A Masters degree, with a significant research component, in a relevant subject area, with overall mark at minimum Distinction.
    • In addition, the mark for the Masters thesis (or equivalent) must be a minimum of 80%.
  • Please note that where a candidate has 70-79% and can provide evidence of research experience to meet equivalency to the minimum first-class honours equivalent (80%+) additional evidence can be submitted and may include independently peer-reviewed publications, research-related awards or prizes and/or professional reports.
  • Language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component).

The potential to engage in innovative research and to complete the PhD within a prescribed period of study

Additional Requirements

The applicant is required to submit a supporting statement as part of their application. Within the supporting statement, candidates should articulate why they believe they are suited for this position. Specifically, we anticipate that the applicant will demonstrate some experience/and or knowledge pertinent to machine learning and will have good programming skills.

How to apply

To find out more about the project, please contact Dr Fei He

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.

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