AlumNode Community Member Elisabeth Wetzer has a PhD fellow offer, please share!

Call for a PhD Position at Integreat - Norwegian Center of Excellence, from Elisabet Wetzer:

The goal of Visual Intelligence is to develop novel deep learning-based solutions to extract knowledge from complex image data to enable new innovations. Deep learning has led to a range of new image-based technologies that are rapidly changing society. Despite these advances, it is still a long way before the potential of deep learning for visual intelligence is realized for applications and industries relying on more complex visual data, e.g., within medicine and health, the marine sciences, the energy sector, and within earth observation.

Visual Intelligence conducts research within machine learning, more specifically within deep learning (deep artificial neural networks). The research focus is broadly on developing the next generation of deep learning solutions in order to:

  • Learn from limited data
  • Capture context and dependencies (e.g., prior knowledge)
  • Enable reliable systems capable of quantifying uncertainty associated with their own predictions and operations, and
  • Develop interpretable learning methodology.


Department of Physics and Technology at UiT in Tromsø, Norway.

About the position:

The focus of this PhD fellowship is on developing deep learning methods for spatio-temporal medical image analysis, such as dynamic positron emission tomography or echocardiography. The PhD student will address challenges in uncertainty prediction, explainability, self-supervised learning, multimodal and temporal information fusion, and the integration of medical domain knowledge to enhance network training and task performance.

The core of the position involves creating new deep learning techniques for spatio-temporal data analysis with limited labels. Key research aspects include estimating and modeling uncertainty, particularly developing new methods for spatio-temporal imaging data. The aim is to establish trustworthy and reliable deep learning solutions for clinical use. Developing explainable methods for spatio-temporal data is a top priority, including method evaluation using medical domain knowledge from experts or additional imaging modalities that provide complementary anatomical information.


For further information about the position, please contact Associate Professor Elisabeth Wetzer:

phone: +47 77 64 48 74
Link to the announcement here.