Awarded Projects

Natural language processing (NLP) has seen tremendous progress recently through the use of complex neural network models that learn effectively from very large amounts of data, but the use of data-intensive large-scale models also gives rise to challenges.

The MOSAIC project (Multimodal Open models for Safe AI in Content generation and retrieval) aims to develop the first large-scale European multimodal foundation models for cross-modal retrieval and image/video generation, with trustworthiness and safety embedded by design.

Understanding if VUS affect proteins and their impact at the molecular level is crucial for expanding our knowledge of cancer, developing personalized prognosis and treatments, or assisting genetic counseling.

This project investigates the architectural foundations needed to build fully open, auditable, and regulation-compliant large language models tailored to Europe’s multilingual landscape.

The project proposes to better understand the fueling of supermassive black holes from galactic scales using multi-scale numerical modelling.

MultiCellFM aims to develop an advanced Cell Foundational Model (CFM) that leverages cutting-edge artificial intelligence to extract meaningful insights from vast single-cell datasets.

The project team proposes a suite of multilingual continual pretrained Dense and Mixture-of-Expert (MoE) models at different size tiers for different types of workloads that have different inference compute constraints.

With their current computer vision and natural language processing models, the project already manages to process 30% of incoming claims automatically.

By combining structural information with mutation-aware modelling at scale, this project aims to deliver a significantly more accurate and biologically grounded variant effect predictor, supporting advances in biomedical research, precision diagnostics, and large-scale functional genomics.