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Machine learning and specifically deep learning techniques are promising tools in medical image analysis and they have demonstrated very good performances in many tasks, such as image segmentation. These techniques are though data demanding and as such they need large-scale cohorts, often multi-centric datasets. In this context, Domain Adaptation (DA) has recently raised strong interests in the medical imaging community as the generalization of algorithms to unseen data (domain shift), different input data domains (missing modalities) and the uncertainty of the networks output due to domain shift are still open problems. Nevertheless, all these aspects are crucial in order to translate AI models for medical image analysis methods to be evaluated in large-scale heterogenous imaging acquired in clinical practice.
In this context, we are looking for a full-time post-doctoral researcher to join the CIBM Signal Processing CHUV-UNIL section. The researcher will focus on domain adaptation, federated learning and other generalization solutions for AI-based reconstruction, segmentation and classification for Magnetic Resonance Imaging (MRI) analysis. This position aims also to investigate different aspects of explainable AI linked to domain shifts. The research will be conducted in the context of AI segmentation and classification models for assessment of advanced imaging biomarkers in Multiple Sclerosis.
How to apply
Please send your CV, two references and a motivation letter to Dr. Meritxell Bach Cuadra (email@example.com).
Open call can be downloaded here.