Postdoctoral position in method development for spatio-temporal medical data

ELLIS; Tromsø; On-site; NOK 520,000 - 700,000; Full time

Job summary

A research institution in Norway is offering a 3-year postdoctoral position focused on method development for spatio-temporal medical data. The role entails developing deep learning algorithms specifically for medical image analysis, including dynamic PET and ultrasound imagery. The successful candidate will engage in innovative research and contribute to ongoing projects in machine learning and computer vision. This position presents an exciting opportunity to advance methodologies in medical imaging.

Qualifications

• PhD in a relevant field such as computer science, machine learning, or related discipline.
• Experience with deep learning techniques and algorithms in medical imaging.
• Ability to work on spatio-temporal data and experience with few-shot learning methods.

Responsibilities

• Develop deep learning algorithms for medical image analysis.
• Collaborate with research teams on innovative methodologies.
• Contribute to the design of machine learning tools for dynamic medical imaging.

Skills

Deep learning algorithms

Machine learning methodologies

Computer vision methodologies

Self-supervised learning

Clustering

Education

PhD in relevant field

Tools

Deep learning frameworks (e.g., TensorFlow, PyTorch)

Job description

Postdoctoral position in method development for spatio-temporal medical data

The UiT Machine Learning Group at the Department of Physics and Technology is offering a 3-year postdoctoral position starting in Fall 2026. The role focuses on developing deep learning algorithms for spatio-temporal medical image analysis, particularly for dynamic PET and ultrasound imagery, with an emphasis on learning from limited labeled data (e.g., few-shot learning, self-supervised learning, and clustering).

The position is based in Tromsø, Norway, and is part of a UiT grant in collaboration with the Center for Research-based Innovation Visual Intelligence. The successful candidate will contribute to ongoing research efforts in designing novel machine learning and computer vision methodologies for medical imaging.

Key references for the project include recent advancements in deep learning for medical imaging and learning from limited labels.