Bioengineering, Computer and Computational Engineering, Electrical and Computer Engineering, Artificial Intelligence and Data Science, and related fields
Work description
The fellow will contribute to developing artificial intelligence-based solutions for the automated analysis of polysomnography (vPSG) videos in the context of diagnosing sleep disorders, particularly REM sleep behavior disorder (RBD). The activities to be performed will include: 1) Training and validation of machine learning models, ensuring the quality and consistency of the datasets used. 2) Development and implementation of computer vision algorithms for detecting and analyzing RBD-specific behaviors in vPSG videos. 3) Integration and testing solutions in a simulated clinical environment, ensuring the applicability of the developed models in clinical practice. 4) Participate in the analysis of results and benchmark the system against manual expert evaluation, aiming to measure gains in accuracy and efficiency. 5) Contribution to the scientific dissemination of the project, including writing scientific articles, preparing presentations for conferences, and supporting the preparation of technical reports.
Academic Qualifications
Students in Bioengineering, Computer and Computer Engineering, Electrical and Computer Engineering, Artificial Intelligence and Data Science, or related fields
Minimum profile required
Knowledge of Computer Vision and Machine Learning
Preference factors
Experience in research activities
Application Period
Since 28 Aug 2025 to 11 Sep 2025
Centre
Telecommunications and Multimedia