| KEYWORD |
Large Language Models (LLMs) for Context-Aided Forecasting in vibration signals
Reference persons MARCO CIVERA
Description Vibration analysis is a critical component in the monitoring and maintenance of mechanical systems, particularly in industries such as aerospace, automotive, civil engineering, and manufacturing. Traditional methods of analyzing vibration signals often rely on statistical techniques and time-series analysis, which may not fully capture the complex relationships within the data. The advent of Large Language Models (LLMs) has opened new avenues for leveraging contextual information to improve forecasting accuracy in various domains. This proposal aims to explore the application of LLMs for context-aided forecasting in vibration signals, enhancing predictive maintenance strategies and operational efficiency, with applications to Aerospace, Civil, and Mechanical Engineering, leveraging competencies typical of Data Engineering and Computer Sciences
Required skills Good knowledge of MATLAB and/or Python
Notes Required grade average: preferably >=27/30 (a qualification interview will be performed as well)
Deadline 27/07/2025
PROPONI LA TUA CANDIDATURA