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Boiling is ubiquitous, from cooking food to industrial processes. Boiling is also an efficient way to enhance heat transfer and, therefore, it is used in a wide range of applications, from cooling electronic devices to refrigeration systems, to heat exchangers in industrial processes.

The near-Earth space is the best reachable plasma laboratory, as it can be probed by constellation satellite missions as well as modelled by high-resolution top-notch supercomputing simulations.

Successful completion of this project would lead to a precise and truly a priori prediction of the low-energy constants (LECs) of chiral perturbation theory (PT) enabling new low-energy tests of the SM.

The engineering outcome of the project will be a web-based tool for predicting friction and heat transfer in air with variable properties, which will leverage the insights from the newly developed DNS dataset.

The birth of the first stars, galaxies and black holes heralded the beginning of the Cosmic Dawn (CD).

The project aims to develop openly accessible world models to foster transparency and collaboration in the field of self-driven cars, addressing key challenges such as data curation and model scalability.

The project aims to develop an advanced foundation model for high-resolution geospatial analysis, with a specific focus on imagery from European regions.

This project combines the state-of-the-art data mining technique called Active Learning with the recently developed FeNNol library for training Machine-Learning-based force fields.

This project will develop the first large-scale foundation models that learn from complete MRI sessions as they occur in hospitals: multiple imaging sequences at their native resolutions, integrated with clinical context such as patient demographics and scanner protocols.

This project develops a scalable foundation model for scientific machine learning on unstructured data, focusing on point cloud representations of physical systems.