Permanent, full-time reader appointment in the School of Informatics, with hybrid working. Research should advance AI methods at the intersection of perception and action. Relevant areas include continual learning, agentic and world models, embodied intelligence, reinforcement learning, decision-making under uncertainty and long-duration autonomy. Responsibilities include original research, undergraduate and postgraduate teaching, and student supervision. The advertised closing time is 23:59 UK time on 20 October 2026.
ePoster · Neuroscience
Humans forage for reward in classic reinforcement learning tasks
Meriam Zid, Veldon-James Laurie, Alix Levine-Champagne, Akram Shourkeshti, Dameon Harrell, Alexander B Herman, Becket Ebitz · COSYNE 2025
Because the world is dynamic and only imperfectly observable, many of the decisions we make are necessarily uncertain. How do we navigate such uncertainty? From the perspective of cognitive neuroscience, the classic answer would be that we evaluate the benefits of each potential choice and then lean towards the one promising the greatest reward, modulo some exploratory noise. Conversely, an ethologist would argue that we would stay with previously rewarding choices until the payout drops below a certain threshold, at which point we start exploring other options. While both hypotheses wield con