Overview:This PhD is funded by the NIHR HealthTech Research Centre in Paediatrics and Child Health. It is being hosted by the Timely Group (PI Dr Anna Moore), and we invite applications for a fully funded PhD studentship in developing responsible AI-informed tools for use in child mental health pathways to support the early identification of mental health problems in children. The post is based in the Department of Psychiatry, commencing in January 2026. This studentship will be under the supervision of Assistant Professor Anna Moore and Professor Zoe Kourtzi (Department of Psychology). The purpose of this PhD is to build early identification tools for childhood anxiety and depression for implementation in clinical pathways. The tools will be designed based on the THRIVE model for children's mental health. Professor Andrew Flewitt from Dept of Engineering will act as an Advisor in Systems Engineering and Professor Peter Fonagy (UCL) will act as an advisor as a key THRIVE author.Timely Group:The Timely Group at the University of Cambridge, Dept. of Psychiatry is led by Dr. Anna Moore, a UKRI Future Leaders Fellow and Assistant Professor in Child Psychiatry and Medical Informatics. Timely is developing innovative approaches to children's mental health through the integration of data science, implementation science and clinical practice. Timely addresses a critical challenge: 70% of children suffering from mental health problems cannot access appropriate services, and those who do face unprecedented waiting times. This research programme aims to revolutionise childhood mental health services through the development of responsible AI-powered digital tools that enable early identification and personalised signposting to evidence-based interventions, improving access to support. The Timely group has led the development of CADRE (Child and Adolescent Data Resource), the first federated multi-agency data platform for children's mental health research. CADRE enables secure analysis of longitudinal data from health, education and social care across multiple trusts and regions. The Timely program is funded by a UKRI Future Leaders Fellowship, DataMind and the NIHR Mental Health Translational Research Collaboration and CADRE is part of the NIHR Mental Health Secure Data Environment.The PhD Project:This PhD project will focus on developing and validating AI tools aligned to the THRIVE framework (Wolpert et al), which will aim to identify depression and anxiety in children and young people, and signpost them to appropriate support based on their needs and preferences for help and support. [Please note, this project is not about building mental health interventions, and so applications should not be focussed on this.] The student will work on creating one of two tools: either designed for use in primary care settings where GPs often struggle to identify mental health needs, or one for children on ASD/ADHD waiting lists who frequently experience unrecognised depression and anxiety whilst facing barriers to accessing mental health support. The project will involve developing and externally validating machine learning algorithms using CADRE's rich longitudinal datasets, and taking systems engineering approaches to ensure tools are practical and user-friendly. The work will take an ethical AI development appraoch, ensuring the tools are transparent, interpretable, and equitable across diverse populations. The student will gain expertise in federated analytics, clinical data science, systems engineering, implementation science, and digital health innovation whilst contributing to research that has direct potential for real-world impact in improving mental health outcomes for children and young people.To apply:We ask for a proposal based on one of the two project options described above. Please draft a proposal that covers your approach to developing the AI algorithms, how you plan to validate them, how you propose to take a responsible AI approach, and how you would employ systems engineering approaches to develop the subsequent tools.We welcome applications from highly motivated and talented candidates interested in conducting innovative research in AI/digital child mental health. The successful candidate will refine their project in collaboration with their supervisors, ensuring alignment with departmental expertise and resources. They will also have the opportunity to participate in regular seminars, workshops, and training sessions designed to support early-career researchers.The studentship covers: - University tuition fees (at the Home rate) PhD in Psychiatry ' Postgraduate Study - A tax-free stipend of £21,737 per annum for 3.5 years - A modest project consumable/equipment and travel budget (£5,000 per year)Applicants should hold (or expect to obtain) the equivalent of a UK first-class or high 2:1 degree in psychology, neuroscience, cognitive science, psychiatry, biomedical sciences, or a related discipline. A relevant Master¿s degree is desirable but not essential. Experience in quantitative statistical methods is essential, and child mental health research experience would be advantageous.We particularly welcome applications from candidates with lived experience of mental health or those with a strong commitment to mental health research.How to applyAll applications should be made online via the University's Applicant Portal for a PhD in Psychiatry, naming Dr Anna Moore and the project within the application. Applications should include academic transcripts, CV, statement of purpose (within the online application) and 2 references. An application is only complete when all supporting documents, including the 2 academic references, are submitted. It is the applicant's responsibility to ensure their referees submit their references before the closing date. Please also explain your motivation why you wish to pursue a PhD in this area, outline your research interests and background, and describe the qualities and experience you will bring to the role. For informal enquiries, please contact Dr Anna Moore ( )Closing Date: 2nd October 2025The University actively supports equality, diversity, and inclusion and encourages applications from all sections of society.Please quote reference RN47122 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.
