Riccardo Rossi awarded SNSF Starting Grant
Riccardo Rossi ©EPFL
EPFL researcher Riccardo Rossi has been awarded a Starting Grant from the Swiss National Science Foundation.
The SNSF Starting Grants represent the SNSF’s highest level of career funding, enabling researchers “to carry out their own independent project and lead a research team in Switzerland.” The Grants are aimed at scientists with several years of research experience during which they have already contributed to scientifically independent and impactful research in their field.
Among the 2025 Starting Grant awardees is Riccardo Rossi, a researcher with EPFL’s Computational Quantum Science Lab.
Rossi’s funded project is titled: “Exploring Correlated Quantum Matter with Novel Diagrammatic and Machine Learning Techniques”.
Project description
The collective behavior of large ensembles of interacting quantum particles gives rise to a wide range of emergent phenomena, from superconductivity to exotic phases such as quantum spin liquids.
Although these many-body systems are central to both fundamental science and technological innovation, unraveling their properties is highly challenging due to the exponential growth of the Hilbert space with system size.
This project focuses on developing advanced theoretical methods, based on recent progress in diagrammatic many-body techniques and machine-learning tools, to address fundamental questions in strongly-correlated quantum matter, with applications ranging from condensed-matter systems to cold-atom simulators.
The collective behavior of large ensembles of interacting quantum particles gives rise to a wide range of emergent phenomena, from superconductivity to exotic phases such as quantum spin liquids.
Although these many-body systems are central to both fundamental science and technological innovation, unraveling their properties is highly challenging due to the exponential growth of the Hilbert space with system size.
This project focuses on developing advanced theoretical methods, based on recent progress in diagrammatic many-body techniques and machine-learning tools, to address fundamental questions in strongly-correlated quantum matter, with applications ranging from condensed-matter systems to cold-atom simulators.