Postdoc in Practical AI-based Reconstruction for High-throughput CT of Natural History Samples

Are you excited by developing novel computational imaging methods that move from research into real-world use? Join our interdisciplinary team as a postdoc to develop advanced image reconstruction algorithms for a novel robotic computed tomography system that will transform how natural history collections are digitized and studied. You will have the opportunity to conduct high-impact research in computational imaging and AI, contribute to widely used open-source software, collaborate with leading researchers in physics and natural history, and publish your work while helping build technology with lasting scientific impact.

Responsibilities and qualifications

We are looking for a highly motivated early-career researcher in advanced image reconstruction methods to join our imaging research group and project “Natural Heritage 3D”. This collaborative project between Visual Computing @ DTU Compute, DTU Physics, Copenhagen University, and the Natural History Museum aims to develop a fully automated robotic computed tomography system along with computational methods to enable 3D digitization of large collections of natural history specimens. 

The Natural History Museum has millions of natural specimens collected over 400 years, which provide an enormous source of information on natural history. CT scans provide a unique method to visualize internal features in 3D in a fully non-destructive manner. In an effort towards digitizing collections and unlocking hidden information on an unprecedented scale, an automated CT system is being developed to scan and analyse large quantities of such specimens. Selected science cases include seal and polar bear craniums as well as snakes and lizards preserved in alcohol.

In addition to experimental CT scan equipment, a key component is the computational pipeline to turn acquired CT projections through reconstruction and image analysis into digital volumes and extract relevant quantitative data. Given the large quantities of specimens, scanning needs to be fast and automated, which demands state-of-the-art reconstruction and analysis methods to handle the data arising from very fast scans taken with few and noisy projections. The focus of this postdoc position is the reconstruction step, conducting research and developing suitable image reconstruction methods to produce sufficiently high-quality 3D volumetric images of natural heritage specimens from the robotic CT system at the DTU 3D Imaging Center.

Your primary activities include:

• Develop new mathematical methods for fast reconstruction of CT data from fast (e.g. sparse-view and/or high-noise) data.
• Develop and validate numerical implementations on simulated and real CT data.
• Explore and compare supervised and self-supervised machine learning/AI-based reconstruction methods with optimization/regularization-based as well as filtered back-projection reconstruction methods.
• Contribute to the development and validation of practical computational reconstruction pipelines for a new fast robotic computed tomography system in the DTU 3D Imaging Centre in collaboration with scientific software developers.
• Contribute to and be part of open-source scientific software communities around the Core Imaging Library and qim3D packages.
• Publish scientific articles within computational methods, data, and applications in collaboration with colleagues in physics and natural history.

Application procedure

Your complete online application must be submitted no later than 15 September 2026 (23:59 Danish time). 

To view the full announcement and to apply, click the 'Apply' button

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