Postdoc: 3D Vision & Segmentation for Natural Heritage

Danmarks Tekniske Universitet; Ørsted; On-site; DKK 420,000 - 520,000; Full time

Job summary

DTU Compute – Department of Mathematics and Computer Science invites a postdoc to advance segmentation and analysis methods for a robotic micro-CT system digitizing natural heritage specimens. You will develop scalable segmentation, human-in-the-loop approaches, and self-supervised models, contributing to open-source software and collaborating across physics, biology, and data science teams.

You will work within the Natural Heritage 3D project, with a 2-year appointment starting 1.

Qualifications

• PhD in computer vision, ideally segmentation and/or quantification of micro-CT data.
• Experience developing deep learning-based image analysis methods.
• Experience with scientific Python programming.
• Experience handling both simulations and large real data.
• Strong spoken and written communication skills in English.
• Strong collaboration skills as well as being self-driven.

Responsibilities

• Develop new segmentation methods for large-scale analysis of micro-CT data.
• Develop and validate methods for efficient human-in-the-loop segmentation of CT images.
• Explore the use of self-supervised foundation models for segmentation with limited labels.
• Establish reference benchmark datasets for natural heritage.
• Collaborate with domain experts in natural heritage and evolutionary biology.
• Contribute to and be part of open-source scientific software communities around the Core Imaging Library and qim3D packages.
• Publish scientific articles within computer vision, data, and applications in collaboration with colleagues in physics and natural history.

Skills

Deep learning

Computer vision

Python programming

Large datasets

English communication

Collaboration

Education

PhD in computer vision

Tools

Python

Job description

DTU Compute – Department of Mathematics and Computer Science invites a postdoc to advance segmentation and analysis methods for a robotic micro-CT system digitizing natural heritage specimens. You will develop scalable segmentation, human-in-the-loop approaches, and self-supervised models, contributing to open-source software and collaborating across physics, biology, and data science teams.

You will work within the Natural Heritage 3D project, with a 2-year appointment starting 1.