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Centre for Neuroscience in Education


João Araújo joined the Centre for Neuroscience in Education in 2020 when he began doing his PhD, under the supervision of Professor Usha Goswami and Professor Ben Simons (Department of Mathematics and Theoretical Physics, University of Cambridge). His thesis combines the latest advances on the neurobiology of speech processing with modern computational modelling approaches to study differences in neural responses to speech in dyslexia / DLD. Through this research João aims to create a clinically-relevant classifier and a remediative intervention for these conditions based on a operant learning brain-computer-interface (BCI).

During his visits to the centre and as part of his research, João has worked upon both the Botnar and the Yidan projects.



João started doing research during his Licentiate degree in Psychological Science in collaboration with the Cognitive Psychology Lab of Universidade de Lisboa on written word recogntion while teaching himself how to code on his spare time. Subsequently, he completed a Master's degree in Neuropsychology, developing a thesis on the temporal modulation structure of illiterate and literate speech in a collaboration between Universidade Católica Portuguesa and the University of Cambridge.  Before he started his PhD, he studied machine learning and computational modelling and completed a Minor in Statistics and Operational Research at Universidade de Lisboa.
Professionally, João got a taste of the start-up world, having developed the software and UX of a futuristic dashboard with a portuguese motorcycle start-up. He also worked at the Champalimaud Centre for the Unknown with a spin-off start-up where he developed a mobile interface and machine-learning classifiers able to detect a user's intention on a BCI task based on on-line neural data recordings (EEG).


Key publications: 

Araújo, J., Flanagan, S., Castro-Caldas, A., & Goswami, U. (2018). The temporal modulation structure of illiterate versus literate adult speech. PLOS ONE, 13(10), e0205224.

Doctoral Student

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