Fully Funded PhD Studentship in Multimodal AI for Early Detection of Neurodevelopmental Disorders

Package: Three-year studentship Job category/type: Research Fully Funded PhD Studentship in AI for Early Detection of Neurodevelopmental Disorders (UK Home Applicants Only) Project: Multimodal AI for Early Detection of Neurodevelopmental Disorders Falcon Foundation Doctoral Programme in Collaboration with The University of Bedfordshire and Luton AI A full-time, fully funded PhD studentship for UK Home students is available at the University of Bedfordshire to develop cutting-edge multimodal AI technologies that could transform the early detection of neurodevelopmental disorders and improve outcomes for children and families. The studentship forms part of the Falcon Foundation Doctoral Programme , an initiative designed to widen access to doctoral study for talented individuals. The successful candidate will also become part of Luton AI , the University of Bedfordshire’s applied AI ecosystem. Luton AI brings together academic research, specialist facilities, external partners and real-world projects to support the responsible development and practical application of artificial intelligence. Through the Falcon Foundation Doctoral Programme, the studentship provides an annual stipend, full tuition fees, academic supervision, access to research infrastructure and specialist facilities, doctoral training, and wider researcher development support. Falcon Foundation is a UK Charity no 1210094 registered with the Fundraising Regulator. Funding The studentship will provide: Full Home University tuition fees for three years – please note, this opportunity is only available to UK Home students. An annual stipend for up to three years. Access to specialist research facilities, computing infrastructure and doctoral training. Academic supervision and support from the University’s research community. The stipend will be awarded annually for up to three years, subject to satisfactory academic progression and in accordance with the agreement with the Falcon Foundation. Year of study Annual stipend Year 1, £21,805 Year 2, £22,895 Year 3, £24,040 Key dates Closing date: Sunday 16 August 2026 Interview date: Virtual interviews will take place during the week commencing Monday 31 August 2026 Expected start date: October 2026 Study mode: Full-time Duration: Three years, subject to satisfactory progression The project Early identification of neurodevelopmental disorders can enable children and families to access specialist assessment, intervention and support at an earlier stage. However, subtle neuromotor indicators can be difficult to identify reliably through visual observation alone. This PhD project aims to develop cutting-edge multimodal AI system capable of analysing infant movements and identifying potential neurodevelopmental risks earlier than may be possible through existing clinical pathways. The successful candidate will work closely with academic and clinical collaborators to support the collection, management and analysis of multimodal research data. The project will investigate the combined use of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will be used to identify clinically relevant indicators of neurodevelopmental risk. Depending on the direction of the research, the project may explore techniques such as convolutional neural networks (CNNs), Vision Transformers, multimodal transformer architectures, time-series analysis and explainable AI. Explainable AI techniques will be incorporated to ensure that the system's outputs are transparent, interpretable and capable of supporting clinical decision-making. The project builds directly on established research in infant motion analysis and explainable AI and benefits from existing collaborations with clinical partners in the UK and USA. These collaborations will provide opportunities to engage with multidisciplinary teams and contribute to research with real-world clinical impact. The longer-term objective is to translate advanced AI research into a practical, affordable and accessible system that could support earlier assessment, guide clinical referrals and improve outcomes for children and their families. Research environment The successful candidate will undertake the project within the University of Bedfordshire’s growing artificial intelligence research and innovation environment and will be connected to the work of Luton AI . Through Luton AI, the candidate will benefit from access to applied AI expertise, advanced computing infrastructure, specialist facilities and a wider network of academic, healthcare, public-sector and industry collaborators. This environment will support the candidate in moving beyond the development of an AI model to consider responsible implementation, clinical relevance, explainability, user needs and the practical translation of research into real-world impact. Engagement with the