The research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. The selected candidate will expand the computational modeling framework Cobel-RL and use it to study how episodic memory might be used to learn to navigate.

A fully-funded PhD is available with Dr Silvia Maggi and Professor Mark Humphries (University of Nottingham) and Dr Hazem Toutonji (University of Sheffield). The project involves understanding how subjects respond to dynamic environments and requires approaches that can track subject's choice strategies at the resolution of single trials. The team recently developed a Bayesian inference algorithm that enables trial-resolution tracking of learning and exploration during learning. This project will build on this work to solve crucial problems of determining which of a set of behavioural strategies a subject is using and how to incorporate evidence uncertainty into its detection of the learning of strategies and transitions between them. Using the extended algorithm on datasets of rodents and humans performing decision tasks will let us test a range of hypotheses for how correct decisions are learnt and what innate strategies are used.

A fully-funded PhD is available with Dr Silvia Maggi and Professor Mark Humphries (University of Nottingham) and Dr Hazem Toutonji (University of Sheffield). The project involves understanding how subjects respond to dynamic environments and requires approaches that can track subject's choice strategies at the resolution of single trials. The project will build on a recently developed Bayesian inference algorithm that enables trial-resolution tracking of learning and exploration during learning. The project aims to solve crucial problems of determining which of a set of behavioural strategies a subject is using and how to incorporate evidence uncertainty into its detection of the learning of strategies and transitions between them. Using the extended algorithm on datasets of rodents and humans performing decision tasks will let us test a range of hypotheses for how correct decisions are learnt and what innate strategies are used.

Doctoral Position in Computational Neuroscience. Are you curious about how the human brain stores memories? Have you wondered how we manage to navigate through space? Our dynamic research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. Currently, we are actively seeking a talented graduate student to join our team, someone who will expand our computational modeling framework Cobel-Spike and use it to study how spiking neural networks can learn to navigate. This position is 65% at TV-L E13, starts as soon as possible, and is funded for 3 years.

We are looking for highly motivated Postdocs or PhD students, interested in computational neuroscience, specifically addressing questions concerning neural circuits underlying perception and learning. The perfect candidate has a strong background in math, physics or computer science (or equivalent), programming skills (python), and a strong interest in biological and neural systems. A background in computational neuroscience is ideal, but not mandatory. Our brain maintains an internal model of the world, based on which it can make predictions about sensory information. These predictions are useful for perception and learning in the uncertain and changing environments in which we evolved. The link between high-level normative theories and cellular-level observations of prediction errors and representations under uncertainty is still missing. The lab uses computational and mathematical tools to model cortical circuits and neural networks on different scales.