POSITION 1, "Graph Representation Learning with Biomedical Applications": The use of Artificial Intelligence (AI) methodology is currently accelerating progress in the area of drug discovery at an impressive speed. Recent successes include the discovery of antibiotics using AI pipelines (Stokes et al., 2020; Liu et al. 2023) as well as the release of the already very impactful AlphaFold model which predicts the three dimensional structure of proteins (Jumper et al., 2021). This rapid scientific progress is also triggering increased industrial interest with Google’s Deepmind announcing the foundation of a new Alphabet subsidiary called Isomorphic Labs with the goal of industrialising AI-driven drug discovery. We are looking for a candidate willing to work in this exciting and dynamic space of scientific progress. Specifically, we would aim to involve the candidate in several projects in which we explore the potential of Graph Representation Learning methodology in the context of Biomedical applications. POSITION 2, "Multimodal Graph Generative Models": Graph generative models are recently gaining significant interest in current application domains. They are commonly used to model social networks, knowledge graphs, and protein-protein interaction networks. The research to be conducted during this project will capitalize on the potential of graph generative models and recent relevant efforts in the Biomedical domain. We will investigate the challenges of multi modality in the context of defining architectures for graph generation under the proper prompt. We expect our designed architectures to be useful in different areas including power grid/telecom/social networks design.