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This project is part of a long-term research program aiming at calculating isospin-breaking and electromagnetic corrections in hadronic quantities from first principles in lattice QCD+QED.
This proposal aims at no less than entirely revolutionising the field of tabular data science, leading the foundation model revolution in this massive and underexplored market.
This project aims to design and develop innovative techniques for training lean neural networks that maintain high accuracy while drastically reducing required computational resources.
The innovation is to treat an experimental observable as a learnable, sequence-predictable quantity that is physically anchored during training yet replaceable by real measurements at inference.
The project's research focuses on advancing 3D image generation, a field with vast potential in virtual reality, movies, robotics simulations, and autonomous driving.
The team's key motivation is to benefit from structural and compositional object-centric representations while modeling frequently occurring interactions between agents such as negotiation scenarios at the intersections.
Fluid mechanics are fundamental to collective behavior in nature and technology ranging from fishschools to wind farms.
LetzAI is a generative AI platform designed with a clear purpose - to give individuals, artists, and brands control over how they are represented in an AI system.
Leveraging high quality internal data of the European Institutions at scale to build an EU institutional large language model (LLM)
This proposal centers on investigating two primary research questions:
(i) the influence of Reynolds number on rough-wall turbulence, emphasizing inner/outer region interactions, and
(ii) the effects of wall curvature and its interplay with pressure gradients.