Postdoctoral position in multimodal and generative artificial intelligence for biomedical imaging and genetics

The research activity involves the study of multimodal and generative models, representation learning, and data-driven approaches applied to medical imaging and heterogeneous biomedical data. The goal is to learn image-derived representations that capture phenotypic information and can be used as predictive proxies for underlying genotypic factors and genetic risk. The emphasis is on developing models that leverage high-dimensional imaging data and contextual information to predict genotype-related information, going beyond traditional statistical association-based approaches.