Postdoc on brain-inspired deep learning for robots

DEEPSELF is a project that lies between machine learning and cognitive science to investigate the emergence of agency in artificial entities by learning hierarchical predictive encodings of events.

Interested contact p.lanillos@donders.ru.nl

Description

Agency appears to enable us to learn, discern and anticipate the consequences of our actions. What should be the internal representation that the agent should learn to plan in different temporal scales? how we can enable robots to answer ‘Did I do it?’ and use that information to interact with the world?

The postdoc will be in charge of developing new brain-inspired machine learning models to allow a robot to learn hierarchical event codes from sensorimotor experience and plan future actions using these event codes. This abilities will be tested in robotic experiments to investigate the emergence of Agency on three levels of abstraction based on human science findings.

The selected candidate will work in an international project team in close collaboration with two PhD students and the principal investigators. The candidate will join an exciting and vibrant young team of experts in machine learning, artificial intelligence and robotics, as well as, be part of a big community of ~20 interdisciplinary research projects under the Active Self DFG priority program umbrella. Thus, the candidate will have the opportunity to take leadership responsibilities and considerably increase the research network. Furthermore, the candidate will participate in summer schools and events organized by the priority program.

We strongly encourage submissions from different representative minorities.