Computer Vision on Medical Imaging
[Open soon]
Work description
The LEIA project (Learning Efficiently from Incomplete Annotations in the Era of Vision Foundation Models), in collaboration with NIAR (Taiwan), has the main objective of investigating semi-supervised learning techniques and how they can be framed within the new paradigm of foundational models. The work plan focuses on developing a benchmark of semi-supervised techniques, developing new methods within this paradigm, and publishing an open repository for public validation of these methods in expert vision scenarios with little annotated data
Academic Qualifications
Mestrado
Minimum profile required
- Masters Degree in Biomedical Engineering, Informatics Engineering, Computer Science, or related;- Masters grade point average equal to or greater than 18/20;- Competences in Python and specifically PyTorch/Tensorflow;- Previous experience in research on computer vision applied to medical imaging (minimum 6 months after conclusion of MSc).
Preference factors
- Experience with semi-supervised learning and foundation models - International experience in research environments.
Application Period
Since 24 Sep 2026 to 08 Oct 2026
[Open soon]
Centre
Biomedical Engineering Research