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 position in Computational Neuroscience is available at the University of Cyprus in collaboration with the University of Surrey (UK), titled “Brain Neuronal Networks Development via Multiscale Agent-based Modelling”. The project aims to demonstrate an innovative computational approach to model and emulate biological neural networks (NNs) by modelling NN development from a single precursor cell. The approach is inspired by the biological brain, using developmental rules encoded in a gene-type manner to reproduce challenging neural complexities. The project will use data from experimental studies and synthetic, simulated data to inform the computational modelling, aiming to create realistic NNs structurally and functionally. Innovative machine learning techniques will be employed to match the in-silico NNs with specific organisms, starting with synthetically generated NNs and increasing biological correspondence. The project will utilize the agent-based modelling software BioDynaMo, an open-source software actively developed for almost a decade.

A fully funded PhD position in Computational Neuroscience is available at the University of Cyprus in collaboration with the University of Surrey (UK), titled “Brain Neuronal Networks Development via Multiscale Agent-based Modelling”. The project aims to model and emulate biological neural networks (NNs) development from a single precursor cell using a computational approach. By leveraging developmental rules encoded in a gene-type manner, the project seeks to reproduce neural complexities found in nature. The computational modelling will utilize data from experimental studies and synthetic, simulated data to inform realistic NNs structurally and functionally. Innovative machine learning techniques will be employed to match in-silico NNs with specific organisms, starting with synthetically generated NNs and increasing biological correspondence iteratively. The project will use the agent-based modelling software BioDynaMo, an open-source software actively developed for almost a decade. This builds on previous work of the supervisory team, including the simulation of a spatially embedded, functional, and biologically realistic neural network that self-organized from a single precursor cell.

In this project we want to study organization and optimization of flexible information processing in neural networks, with specific focus on the visual system. You will use network modelling, numerical simulation, and mathematical analysis to investigate fundamental aspects of flexible computation such as task-dependent coordination of multiple brain areas for efficient information processing, as well as the emergence of flexible circuits originating from learning schemes which simultaneously optimize for function and flexibility. These studies will be complemented by biophysically realistic modelling and data analysis in collaboration with experimental work done in the lab of Prof. Dr. Andreas Kreiter, also at the University of Bremen. Here we will investigate selective attention as a central aspect of flexibility in the visual system, involving task-dependent coordination of multiple visual areas.

The Computational Neurophysics lab at the University of Bremen headed by Dr. Udo Ernst offers at the earliest date possible: Postdoc / PhD student in Computational Neuroscience for 3 years. In this project we want to study organization and optimization of flexible information processing in neural networks, with specific focus on the visual system. You will use network modelling, numerical simulation, and mathematical analysis to investigate fundamental aspects of flexible computation such as task-dependent coordination of multiple brain areas for efficient information processing, as well as the emergence of flexible circuits originating from learning schemes which simultaneously optimize for function and flexibility. These studies will be complemented by biophysically realistic modelling and data analysis in collaboration with experimental work. Here we will investigate selective attention as a central aspect of flexibility in the visual system, involving task-dependent coordination of multiple visual areas.

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.