Student Research Assistant Position

for the organisations of a challenge and an ongoing reasearch project in our group (40h per month)

Project description

Data scarcity is one of the major limiting factors preventing application of powerful machine learning algorithms to many medical applications beyond a handful of big public datasets. Cross-Domain Few-shot Learning (CD-FSL) offers the potential to exploit similarities between different medical image analysis datasets and leverage shared knowledge to learn previously unseen tasks more efficiently. However, CD-FSL is underexplored in medical image analysis. We recently released the MIMeta Dataset, the first medical image cross-domain few-shot learning benchmark, and started a challenge. With the L2L challenge we want to encourage the medical image analysis and machine learning communities to explore the potential of CD-FSL approaches in the promising application domain of medical image analysis, and to develop algorithms that are robust to the extremely high task and data diversity encountered in this domain. The L2L Challenge is an official MICCAI 2023 challenge. The International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) is the top conference in the domain of medical image analysis.

For more information visit our challenge website.

Your tasks

You will aid in building the evaluation system for the challenge and writing new features for the MIMeta and torchcross PyTorch libraries. Additionally you will help with technincal support for challenge participants.

Your profile

  • Good knowledge of Machine Learning and of Image Analysis/Computer Vision,
  • Interest in working with medical imaging datasets,
  • Interest in learning about and working on few-shot learning and/or meta-learning,
  • Proficiency in Python, PyTorch and Git.

What we offer

  • HiWi salary according to the standard rates of the University of Tübingen,
  • A desk space in the Tübingen AI Research Building,
  • Insights into an exciting and trending research field,
  • The possibility of contributing to a scientific publication.

How to apply

If interested, please contact Stefano Woerner and attach your CV and transcript of records to apply. Only currently enrolled students of the University of Tübingen can be considered for this position.

Stefano Woerner
Stefano Woerner
PhD student