Two USI researchers awarded SNSF Postdoc.Mobility 2025 grant

Chiara Mercuri and Cristian Tirelli, currently PhD students at Università della Svizzera italiana (USI), have been awarded funding through the SNSF Postdoc.Mobility programme.

The call, promoted by the Swiss National Science Foundation (SNSF), supports young researchers who are about to complete their PhD or have recently earned it, offering them the opportunity to conduct a research period abroad. It is a highly competitive programme, with a success rate of 36.2%.

Understanding how we discuss sustainability online

Chiara Mercuri, a doctoral student at the Faculty of Communication, Culture and Society - Institute of Argumentation, Linguistics and Semiotics, will spend two years, beginning on 1 February 2026, at the Université catholique de Louvain. Her project, funded with CHF 111,070, is titled: "Negotiating agentivity: an argumentative study of emergent common ground in online interactions about sustainability".

Her research examines how people attribute responsibility when discussing sustainability online. When we talk about climate change or the environment, we do not all share the same starting point: some attribute responsibility to governments, others to companies, and others to individual citizens. These differences can lead to misunderstandings and stall dialogue.

The project will analyse online discussions, particularly on Reddit, to understand how these differences emerge, how users address them, and whether, through discussion, it is possible to build a common understanding. The aim is to gain insights into how digital conversations function and identify strategies to foster more constructive dialogue on complex issues such as sustainability.

Making machine learning more efficient in edge devices

Cristian Tirelli, a doctoral student at the Faculty of Informatics - Computer Systems Institute, will spend two years, from 1 July 2026, at the University of California, San Diego. The project, funded with CHF 135,600, is titled "Machine learning acceleration at the edge through Coarse-Grain Reconfigurable Arrays".

His research focuses on machine learning, the technology behind many artificial intelligence applications, such as speech and image recognition. Increasingly, these systems need to run directly on local devices, such as smartphones, sensors or industrial equipment, without relying on large-scale data centres. In such cases, however, there are significant constraints regarding energy, memory, and speed.

The project aims to develop new open-source software tools that better leverage specific types of flexible hardware, enabling artificial intelligence to run faster, use less energy, and adapt more easily across different devices. This will enable the use of advanced artificial intelligence models even in resource-limited contexts.