The calls for applications are now online for three research grants for the COLLAGE project – Continual, Decentralized Compositionality for Sustainable Artificial Intelligence. This project is funded by the Ministry of University and Research (MUR) under the Italian Science Fund – FIS 2 Call. Interested candidates must submit their applications by Friday, June 19, 2026, at 12:00 PM (CET, UTC+1). 

COLLAGE explores sustainable AI methodologies that leverage continuous learning and decentralized model composability. The goal is to efficiently customize and update AI models, even with limited data and computational resources. 

The comparative evaluation procedures are aimed at awarding three scholarships for research activities related to the following topics: 

• ADMIX: A Decentralized AI Model Index and Benchmark for Expert Models Discoverability – duration: 3 months; 
• Continual Fine-Tuning Methods for Large Language Models – duration: 3 months; 
• Continual Fine-Tuning Algorithms at Ultra-low Scale – duration: 6 months. The activities will be conducted remotely. 

The scientific lead for the project is Prof. Vincenzo Lomonaco. 

The calls for applications and application forms are available here. The following documents must be attached to the application for admission to the selection process: 

• a dated and signed curriculum vitae, including authorization for the processing of personal data; 
• a copy of a valid identity document; 
• certification of the degree obtained (or a self-declaration), or a comparable academic qualification obtained abroad; 
• a list of any scientific publications (you can apply even if you don't have any publications); 
• electronic copies of up to three academic papers deemed relevant for evaluation, including theses and/or dissertations; 
• a declaration regarding any conflicts of interest or situations of incompatibility. 

Admission requires a Master’s degree in the scientific-disciplinary field 01/INFO-01 or 09/IINF-05, or an equivalent academic qualification obtained abroad. Proven experience in Machine Learning and AI is considered a preferred qualification. 

The selections, made by a dedicated Evaluation Committee, will be based solely on qualifications (evaluation of the curriculum vitae and scientific publications); there will be no oral interview. The selection results and final rankings will be posted on the dedicated web page.