Hunting for patient subtypes through 3D imaging biomarkers in genetic disorders (ITN-PhD Position @ BIO3 KU Leuven)

Within this PhD project, we aim to develop a framework for gene-centric image data integration analytics and to support a patient stratification strategy that identifies major gene effects with potential applications in a variety of medical disorders. This will built on our previous work on rare monogenetic/ complex diseases in craniofacial and neurodevelopmental disorders, using available extensive datasets of different imaging modalities on individuals and patient groups. Mathematics, statistical genetics and deep learning in image analysis (potentially non-linear data-dependencies) will enable data-driven phenotyping from images for patient diagnostics, and stratified screening/subtyping.
The project outcome will lead to an improved understanding of the genetic architecture of the human face and brain and the effects of genome based stratifications in ‘the normal’ population. Furthermore, new data-driven approaches will identify imaging biomarkers for patient diagnostics and subtyping taking normal-range variations and stratifications into account.

Application info: and

Comments are Closed