Manifold based modelling of fNIRS neuroimages

About the Project

In order to answer the myriad of specific research questions related to the brain as observed with fNIRS, current fNIRS analysis is based on ad-hoc models, whether from statistics e.g. like the general linear model, or other branches of maths e.g. graph theory, differential equations, etc in each case with associate and causal variants. However, upon closer look, that myriad of questions can be elegantly grouped into a very reduced number of mathematical problems and moreover, these problems share the same foundations. With this insight it is a fair question to ask whether we can develop some universal model that can answer all these fNIRS-based research questions at once? In our past research we have been able to develop generic manifold based models capable to answer each of these individual mathematical problems one at a time, but the universal model has thus not been formulated.

This project intends to develop such mathematical model by tapping into other topological sets and/or group theory. In order to do so, consolidation of the previous models under a common form is needed. Then, a higher (more abstract) model should emerge that generalises the individual solutions. Finally, validation on fNIRS neuroimages should be established. To realize these goals, contributions in computational neuroimaging, algorithmic processing, and data analysis are needed in a truly multidisciplinary project requiring knowledge from fields as diverse as optics, mathematics, neurosciences, and computer sciences.

Applicants should have a strong background in mathematics and in particular in topology and analytical geometry, and being enthusiastic about computer sciences. You should have a commitment to research in analyzing fNIRS neuroimaging data with applications in neuroscience. You must have exceptional programming (preferably MATLAB) and communication skills.

Expected starting date: September 2025