How can a network of cells perceive, represent, and make sense of the world around it?
Experimental techniques such as high-resolution, multi-modal magnetic resonance imaging, light-sheet microscopy, or single-cell sequencing provide unprecedented insights into the structures and processes of neural systems and life in general. However, these methods yield high-dimensional data with intricate structures and dependencies that can be challenging to interpret.
We want to help uncover the underlying patterns. With our research, we want to contribute conceptual and computational tools to map, explore, and understand the manifolds of neural data that help us improve neuroscience and AI research and clinical practice in three complementary areas:
- Analysis: Mapping structure and function of neural systems across scales
- Exploration: Visualizing the geometry of complex biological systems
- Synthesis: Computational modelling of neural development and cognition
Available PhD projects
Potential PhD projects include the following areas:
- Scientific Visualisation: Developing computational tools to explore the manifolds and topological structures underlying neural data (neuroimaging data and representations in computational neural network models).
- Artificial Intelligence: Establishing deep (reinforcement) learning as a virtual model organism to study processes of representation learning and assess quantitative methods in cognitive neuroimaging.
- Neuroinformatics: Developing computational tools (e.g. geometric deep learning) that integrate statistical and knowledge representations to map and analyse multi-modal data (e.g. structural, functional, genetic, behavioural) across scales, individuals, and species.
- Statistical Computing: Exploring probabilistic programming to build computational models in cognitive science and neuroimaging.
- Computational Neuroscience: Exploring computational models of neural network development and function to build and validate neural data analysis methods.