Post-doctoral Researcher Generalizing Deep Learning for Magnetic Resonance Image Analysis

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Post-doctoral Researcher Generalizing Deep Learning for Magnetic Resonance Image Analysis

Organization: 
University of Lausanne
Country of Position: 
Switzerland
Contact Name: 
Meritxell Bach
Subject Area: 
Bio Imaging and Signal Processing
Computational Imaging
Machine Learning for Signal Processing
Start Date: 
March 08, 2022
Expiration Date: 
April 29, 2022
Position Description: 

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.

Your profile

  • A PhD degree in engineering, electrical engineering, computer science, physics or related fields
  • Strong background in image processing and deep learning techniques is a must, with published papers in key journals (TMI, MedIA, etc) and conferences in the field (MICCAI, MIDL, NEURIPS, CVPR, etc).
  • Demonstrated previous experience in different aspects of domain adaptation in reconstruction/segmentation or classification problems is required.
  • Experience in neuroimage analysis is a plus.
  • You certify proficiency in programming (Python, PyTorch/Keras, Javascript, bash, etc)
  • You are eager to supervise and transfer your knowledge to master and PhD students and promoting a collaborative environment within CIBM sections.
  • You have excellent written and oral communication skills in English; French is a plus.
  • Rigorous work habits, a curious and critical mind, and a good sense of initiative.
  • A high-level perseverance and a strong personal commitment are expected.

How to apply

Please send your CV, two references and a motivation letter to Dr. Meritxell Bach Cuadra (meritxell.bachcuadra@unil.ch).

Open call can be downloaded here.

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