Guillaume Joyet holds a bachelor's degree in computational sciences from the University of Basel and is currently pursuing a master's degree in data science. He joined the Translational Psychiatry Research Group as part of his master's thesis.

His project focuses on enhancing the decoding of behavioural outcomes from electroencephalogram data using advanced machine learning techniques. These include multivariate pattern analysis, recurrent neural networks, and transformer architectures. The latter are also being explored through a foundation model approach, where the model is initially pre-trained on a large, diverse EEG dataset following a self-supervised paradigm, and subsequently fine-tuned for specific downstream tasks.