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This project proposes the development of a Phylogenetic Foundation Model, an FM for eukaryotic genomes that natively embeds cross-species conservation and divergence.
This project will support a wide range of research in cosmology, fostering technological advancements in data analysis and simulation techniques.
One critical yet largely unexplored aspect of fusion research is related to the impact of fusion-born alpha particles on plasma confinement – a pivotal concern given their role as primary heating mechanism in fusion reactors.
The study proposes to advance pixel-level generative models that generate images directly in the pixel domain, targeting high-fidelity details and scalable training.
In the last decades, ionic liquids (ILs) and their close relatives deep eutectic solvents (DES) have increasingly attracted a huge research effort due to their varied applicability in many technological fields of societal importance.
How did the Earth and the other terrestrial planets form? Even though there are many models in existence that attempt to solve this question, all of them suffer from specific shortages that can only be overcome with next-generation computation facilities.
This project's precise analysis will enable precise and robust KiDS cosmology and establish the methodology for billion-galaxy catalogues from the Vera Rubin Observatory's Legacy Survey of Space and Time
In this project we will make use of quantum anharmonic first-principles calculations to predict novel high-Tc ternary hydrides stable or metastable at ambient pressure.
Document Understanding involves analyzing documents to extract and interpret textual content, complex structures, layouts, graphical elements, and handwritten information.
Current Large Language Models (LLMs) are trained on massive amounts of text data, mainly encompassing only a few dominant languages. Studies suggest that this over-reliance on high-resource languages, such as English, hampers the performance of LLMs in mid- and low-resource languages.