This work explores novel applications of Artificial Intelligence, specifically transformer-based models, for objective assessment of athletes’ internal load and emotional states. 
This work explores the transformative impact of Large Language Models (LLMs) on AI-driven coaching, positioning them as a fundamental shift in how knowledge is structured and utilized within sports science. 
This work introduces an innovative framework within Sports Science 3.0 for enhancing individualized training optimization through the integration of Artificial Intelligence (AI) with physiological metrics. 
Can we use optimal control and vehicle dynamics principles to position barriers for cycling races? 
Can we predict/estimate the contribution of height/weight on time trial performance? 
We are fascinated by racing lines and trajectories 
Application of deep learning technologies to the process of cardiopulmonary exercise tests 
Application of optimal control techniques to the study of cycling trajectories 
