Bioengineering, Computer Engineering and Computing, Electrical and Computing, Artificial Intelligence and Data Science and related areas
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Work description
The fellow to be hired will contribute to the development of artificial intelligence-based solutions for automated analysis of polysomnography (vPSG) videos in the context of the diagnosis of sleep disorders, in particular REM sleep behavior disorder (RBD). The activities to be performed aim to: 1) Annotation and preparation of data for training and validation of machine learning models, ensuring the quality and coherence of the data sets used. 2) Development and implementation of computer vision algorithms for detecting and analyzing specific RBD behaviors in vPSG videos. 3) Integration and testing of solutions in a simulated clinical environment, ensuring the applicability of the models developed in medical practice. 4) Participation in the analysis of results and in the benchmarking of the system against the manual evaluation of experts, aiming to measure gains in precision 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
Student in Bioengineering, Computer Engineering and Computing, Electrical and Computing, Artificial Intelligence and Data Science and related areas
Minimum profile required
Experience in Computer Vision and machine learning.
Preference factors
Experience in research projects and writing of scientific papers.
Application Period
Since 02 May 2025 to 15 May 2025
[Open soon]
Centre
Telecommunications and Multimedia