PhD position, ‘Computationally realistic architectures for a Bayesian brain’

PhD position ‘Computationally realistic architectures for a Bayesian brain’

Faculty of Social Sciences
Vacancy number: 24.21.13
Closing date: 14 July 2013

This PhD project aims to advance our understanding of the computational foundations of probabilistic inference and learning in the brain. According to current theory, even only approximately computing probabilistic inferences is computationally intractable for situations of real-world complexity. This is in marked contrast to the efficiency of inference and learning as done by the brain in practice. The objective of the project is to resolve this paradox by developing a new theory that explains the efficiency of inference and learning as done by the brain in practice. Using an innovative approach that combines formal modeling, parameterized complexity analysis and computer simulation, we aim to identify parameters of a computational architecture that can make a probabilistic brain computationally efficient. The project will furthermore involve conceptual (philosophical) analysis to derive the implications of this new theory for current debates in the philosophy of cognitive science.