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

About the role

The successful candidate will develop cutting-edge multimodal AI systems capable of analyzing infant movements and identifying potential neurodevelopmental risks earlier than may be possible through existing clinical pathways. The project will involve working closely with academic and clinical collaborators to support the collection, management, and analysis of multimodal research data.

Responsibilities

• Develop multimodal AI models for the early detection of neurodevelopmental risks.
• Investigate computer-vision methods for analyzing infant movement and neuromotor patterns.
• Integrate video data with information collected through low-cost sensors.
• Develop explainable AI approaches that produce transparent and clinically meaningful outputs.
• Evaluate the reliability, accuracy, and practical applicability of the proposed system.
• Work with academic and clinical collaborators to support the translation of the research into healthcare practice.
• Publish research findings in relevant peer-reviewed journals and conferences.
• Communicate research progress and outcomes to academic, clinical, and non-specialist audiences.

Requirements

Applicants should normally have:

• A good honours degree, normally at least a UK 2:1 or international equivalent, in computer science, artificial intelligence, data science, biomedical engineering, electronic engineering or a closely related subject.
• A relevant master’s degree, or equivalent research or professional experience, would be advantageous.
• Knowledge of artificial intelligence and machine learning, computer vision and video analysis, deep learning, multimodal data analysis, signal processing or sensor-data analysis, explainable or responsible AI, and healthcare, biomedical, or clinical data applications.

Qualifications

• A good honours degree, normally at least a UK 2:1 or international equivalent, in computer science, artificial intelligence, data science, biomedical engineering, electronic engineering or a closely related subject.
• A relevant master’s degree, or equivalent research or professional experience, would be advantageous.
• Knowledge of artificial intelligence and machine learning, computer vision and video analysis, deep learning, multimodal data analysis, signal processing or sensor-data analysis, explainable or responsible AI, and healthcare, biomedical, or clinical data applications.

Skills and Competencies

• A logical, analytical, and methodical approach to problem-solving.
• The ability to plan and undertake research independently.
• Strong written and verbal communication skills.
• The ability to explain complex technical ideas clearly to specialist and non-specialist audiences.
• Careful research-data management and record-keeping.
• The ability to work effectively with academic, technical, and clinical collaborators.
• An understanding of the ethical and responsible use of AI, particularly in healthcare.
• A high level of motivation, initiative, and intellectual curiosity.
• The ability to manage competing priorities and work to agreed deadlines.
• A willingness to learn new technical and research methods.
• A willingness to engage proactively and professionally with the Falcon Foundation throughout the studentship.

Benefits

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.

Pay

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.

Schedule

The project will be supervised by Dr Massoud Khodadadzadeh and Dr Edward Braund.

Contact

Prospective applicants are welcome to contact Dr Khodadadzadeh or Dr Braund for an informal discussion about the project before submitting an application.