Learning procedural knowledge through human-AI interaction

The candidate will learn to develop novel techniques in computer vision, machine learning and human-machine interaction for the task learning. Relevant domains of research include visual recognition, visual question answering, human-in-the-loop reinforcement learning, augmented reality, and human-machine interaction. The student will explore novel application cases such as assisted living in the domestic setting, collaborative robots in industries, and augmented learning.

Years 1 and 3 are based in the UK and Year 2 in Singapore.

Please read the advert for full information.

Please ensure you quote FNS_A*Star PhD studentship on your application.