GN Nenadic

Join a pioneering PhD project at The University of Manchester to develop Proactive Brain-Computer Interfaces (BCIs) that move beyond passive signal decoding and define the future of human-AI symbiosis. This research aims to overcome the cognitive bottleneck of current assistive technologies by creating a Neuro-Agentic Framework—an AI system that collaborates with users, infers high-level intent from neural activity, and autonomously plans and executes complex real-world tasks. Unlike traditional BCIs that require users to micromanage every action, this project will focus on building intelligent partners capable of intent-to-action reasoning. You will design architectures that bridge biological neural states and digital or robotic actions, leveraging Large Action Models. The research will utilize Structured State Space Models (SSMs) and Transformers to enable continuous control, long-term context maintenance, and real-time adaptation to user needs and changing environments. Hierarchical autonomy will be achieved through a Mixture-of-Experts (MoE) system, where the human provides the 'why' and the Agentic AI handles the 'how.' This is a unique opportunity to work at the intersection of Generative AI, Control Theory, and Neuroscience, transforming BCIs from input devices into autonomous agents that restore independence and enhance human capabilities. The project is based in the Department of Computer Science and is supervised by Dr J Sun, Dr Z Li, Dr A Casson, and Prof GN Nenadic. Eligibility: Applicants should have a First or Upper Second Class Honours degree (or equivalent) in Computer Science, Robotics, Physics, Engineering, or Mathematics. Proficiency in Python is essential, and knowledge of Reinforcement Learning, control systems, or agent-based modeling is highly desirable. Creative thinkers eager to tackle real-time, closed-loop AI challenges are encouraged to apply. English language certification is required if applicable. Funding: Excellent candidates will be nominated for competence-based faculty funding, covering tuition fees and providing a tax-free stipend at the UKRI rate (£20,780 for 2025/26), with expected annual increases. Self-funded students are also welcome to apply. The start date is October 2026. Application Process: Contact Dr Jingyuan Sun at [email protected] with your CV before applying. Apply online via the university's application system, specifying the project title and supervisor. Submit all required documents, including transcripts, CV, supporting statement, and referee details. Complete the additional information form as instructed. The deadline for applications is 28 February 2026, but early application is recommended as the advert may close sooner. The University of Manchester values equality, diversity, and inclusion, and encourages applicants from all backgrounds. Flexible study arrangements may be considered depending on the project and funding.

This PhD position at The University of Manchester offers an exceptional opportunity to address the challenge of patient heterogeneity in neurodegenerative diseases through the development of foundational AI models. The project is situated at the intersection of artificial intelligence, bioinformatics, and clinical neuroscience, aiming to create a sophisticated 'Digital Twin'—a dynamic health profile that mirrors an individual's condition over time. The successful candidate will integrate multi-modal data, including neuroimaging, multi-omic profiles, and electronic health records, to generate personalised predictions of disease progression. This research is poised to revolutionise precision medicine by providing new tools for forecasting, understanding, and managing neurodegenerative conditions. As part of a highly interdisciplinary supervisory team, you will work with state-of-the-art AI technologies and leverage unparalleled clinical datasets. The project is ideal for candidates with a strong quantitative background, creativity, and a passion for collaborative, multidisciplinary research. Essential skills include proficiency in Python, experience with machine learning and deep learning frameworks (such as PyTorch and TensorFlow), and a solid foundation in mathematical principles underlying AI and statistical modelling. Desirable experience includes advanced deep learning architectures, large-scale AI models, bioinformatics, and handling complex clinical or biomedical data. The position is fully funded for 3.5 years, with excellent candidates nominated for competence-based funding. The University of Manchester offers a range of scholarships, studentships, and awards to support both UK and overseas postgraduate researchers. Flexible study arrangements, including part-time options, may be considered depending on the project and funding. Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering discipline. English language certification is required if applicable. The application deadline is March 16, 2026, with a start date in October 2026. Early application is recommended as the advert will be removed once the position is filled. To apply, contact Dr. Jingyuan Sun ([email protected]) before submitting your application to discuss your motivation and background. Applications must be submitted online, specifying the project title, supervisor, funding status, previous study details, and contact details for two referees. Required supporting documents include transcripts, CV, supporting statement, and English language certificate if applicable. For further information, visit the project page or the university's application portal. The University of Manchester is committed to equality, diversity, and inclusion, actively encouraging applicants from diverse backgrounds and career paths. Flexible study arrangements are supported, and applications from those returning from a career break or other roles are welcomed.