| KEYWORD |
Fine-Tuned Foundation Model for Automated Biofabrication Protocol Generation
keywords AI, BIOFABRICATION, FOUNDATION MODELS, GENERATIVE AI, PROCESS DESIGN
Reference persons ROBERTA BARDINI, STEFANO DI CARLO, ALESSANDRO SAVINO
External reference persons Riccardo Smeriglio
Research Groups DAUIN - GR-24 - reSilient coMputer archItectures and LIfE Sci - SMILIES
Description This thesis proposal focuses on harnessing cutting-edge artificial intelligence techniques to streamline and expedite the design of biofabrication protocols. The development of effective biofabrication methods relies heavily on trial-and-error experimentation, which can be both costly and time-consuming. This project aims to create a generative model that produces efficient, reliable, and context-specific biofabrication protocols by leveraging advanced foundation models and their fine-tuning capabilities.
Throughout the project, the candidate will refine training strategies and develop fine-tuning algorithms to adapt large-scale, pre-trained language models to the intricacies of biofabrication. This involves curating domain-specific datasets, engineering prompts, and evaluating performance metrics to ensure high-quality protocol generation. Mastering these techniques will enable the candidate to gain deep insight into artificial intelligence methodologies, large language models, and their potential for accelerating research and development in biofabrication, ultimately improving the reproducibility and scalability of advanced tissue and material engineering techniques.
Deadline 10/12/2025
PROPONI LA TUA CANDIDATURA