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A big data approach to women’s health among individuals with ADHD

Project Overview 

Attention-deficit hyperactivity disorder (ADHD) is a prevalent condition that is associated with adverse physical and mental health outcomes. While previously thought of as a childhood disorder, there is increasing recognition that ADHD also affects adults, and while in childhood there is a strong male gender bias of ADHD diagnosis, by adulthood the gender ratio has largely equalised. However, because of a historical focus on ADHD amongst boys, women with ADHD are a critically understudied population, and factors that affect women specifically are under-researched, especially related to times of hormonal change.

This PhD will harness the growing availability of ‘big data’ related to ADHD and women’s health to improve outcomes for women with ADHD, especially related to times of hormonal change including puberty, the menstrual cycle, the perinatal period and menopause. Data for the PhD projects, depending on the student’s interest, can include: remote monitoring data collected from wearables and smart phones apps related to ADHD symptoms and the menstrual cycle; genetic data, e.g. for constructing polygenic risk scores; and data from large population-based medical registries. Potential statistical methods could include intensive longitudinal data analysis and machine learning techniques.

Research Environment

The student will participate in lab meetings with Dr Jessica Agnew-Blais's ADHD Life Lab, which includes PhD students, postdocs and RAs, as well as larger cross-group weekly meetings focused on child and youth development. There will also be additional training opportunities focused on developing methodological skills both within QMUL and at other universities in the UK. The student will be able to attend national and international conferences related to their projects.

The School of Biological and Behavioural Sciences at Queen Mary is one of the UK’s elite research centres, according to the 2021 Research Excellence Framework (REF). We offer a multi-disciplinary research environment and have approximately 180 PhD students working on projects in the biological and psychological sciences. Our students have access to a variety of research facilities supported by experienced staff, as well as a range of student support services. Our PhD students become part of Queen Mary’s Doctoral College which provides training and development opportunities, advice on funding, and financial support for research.

Find out more about the School of Biological and Behavioural Sciences on our website.

Keywords:  Women’s health, neurodevelopment, menopause, menstrual cycle. 

Funding & Eligibility

Queen Mary University of London has partnered with the China Scholarship Council (CSC) to offer a joint scholarship programme to enable Chinese students to study for a PhD programme at Queen Mary.  Under the scheme, Queen Mary will provide scholarships to cover all tuition fees, whilst the CSC will provide living expenses and one return flight ticket to successful applicants.

Applicants must:

  • Be applying for CSC funding.
  • Be a citizen and permanent resident of the People’s Republic of China and hold a Chinese passport.
  • Satisfy all eligibility criteria set out by the CSC and must refer to the CSC website for full details.
  • Apply to QMUL by 28th January 2026. Late applications will not be considered.
  • Submit ALL required documentation, including evidence of their English Language ability ahead of the CSC application deadline.

CSC application rules differ slightly for domestic applicants (students applying from China) and overseas applicants (students applying from overseas). Therefore, ALL applicants are advised to see the CSC website for full details on eligibility and conditions on the scholarship. 

Entry Requirements

Applications are invited from outstanding candidates with or expecting to receive a first or upper-second class honours degree in an area relevant to the project, for example statistics, bioinformatics, data science, epidemiology, computer science, or statistical genetics. A master’s degree is desirable, but not essential.

Some knowledge of or experience with statistics and/or data science is required.

You must meet the IELTS requirements for your course and upload evidence before CSC’s application deadline, ideally by 1st March 2026. You are therefore strongly advised to sit an approved English Language test as soon as possible, where your IELTS test must still be valid when you enrol for the programme.

Please find further details on our English Language requirements page.

How to Apply

Formal applications must be submitted through our online form by 28th January 2026 for consideration. Please identify yourself as a ‘CSC Scholar’ in the funding section of the application.

Applicants are required to submit the following documents:

  • Your CV
  • Personal Statement
  • Evidence of English Language e.g.) IELTS Certificate
  • Copies of academic transcripts and degree certificates
  • References 

Find out more about our application process on our SBBS website.

Informal enquiries about the project can be sent to Dr Jessica Agnew-Blais

Admissions-related queries can be sent to sbbs-pgadmissions@qmul.ac.uk 

Shortlisted applicants will be invited for a formal interview by the supervisor. If you are successful in your QMUL application, then you will be issued an QMUL Offer Letter, conditional on securing a CSC scholarship along with academic conditions still required to meet our entry requirements.

Once applicants have obtained their QMUL Offer Letter, they should then apply to CSC for the scholarship with the support of the supervisor.

For further information, please go to the QMUL China Scholarship Council webpage.

Apply online


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