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source

3Shape

Postdoc in Uncertainty-Aware Optimization for Inverse Problems in 3D Scanning 3Shape in Denmark

Degree Level

Postdoc

Field of study

Computer Science

Funding

Industrial postdoctoral position partially funded by Innovation Fund Denmark. The appointment is for 2 years and is a fixed-term role starting in January 2027, with small variations possible. No stipend amount is stated.

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Country

Denmark

University

3Shape

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Keywords

Computer Science
Biomedical Engineering
Mathematics
Uncertainty Analysis
Linear Algebra
Optimisation
Computational Imaging
Statistics
Inverse Problem
Physics
ML

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About this position

Postdoc opportunity in uncertainty-aware optimization for inverse problems in next-generation 3D scanning at 3Shape in Copenhagen, Denmark, in collaboration with DTU Compute, Technical University of Denmark.

This industrial postdoctoral project focuses on numerical linear algebra, optimization, inverse problems, uncertainty quantification, and computational imaging. The research aims to develop new mathematical and computational tools for characterizing measurement uncertainty, propagating uncertainty through iterative solvers, and improving the reliability of reconstructed 3D models and medical 3D reconstruction pipelines.

The successful candidate will work on uncertainty-aware methods for large-scale linear least squares problems, including Krylov subspace methods and related iterative algorithms. The role combines theory, simulation, and software implementation, with opportunities to contribute to scientific publications, technical reports, prototype development, and innovation activities with industrial impact.

Eligibility highlights: applicants must hold a PhD in Mathematics, Statistics, or a related discipline; the PhD must have been awarded after March 2021 or be completed by November 2026. Strong programming skills in at least one language such as C#, C++, or Python are expected. Experience or strong interest in statistical modelling, probabilistic methods, machine learning, and uncertainty quantification is an advantage.

Funding: the position is a 2-year fixed-term industrial postdoc, partially funded by Innovation Fund Denmark. No stipend amount is stated.

How to apply: submit your application through the 3Shape careers portal. The posting asks applicants not to include photos or sensitive personal information such as age, marital status, or nationality.

Funding details

Industrial postdoctoral position partially funded by Innovation Fund Denmark. The appointment is for 2 years and is a fixed-term role starting in January 2027, with small variations possible. No stipend amount is stated.

What's required

Applicants must hold a PhD in Mathematics, Statistics, or a related discipline; the PhD must have been awarded after March 2021 or be obtained within November 2026. Strong expertise in numerical linear algebra, optimization, and/or inverse problems is required, along with solid understanding of linear least squares methods and iterative solvers, ideally Krylov subspace methods. Experience with or strong interest in uncertainty quantification, statistical modelling, or probabilistic methods is expected. Strong programming skills in at least one language, ideally two, such as C#, C++, or Python, are required. Familiarity with machine learning is an advantage. Candidates should be able to work independently, take ownership of research problems, collaborate in an industrial-academic environment, and communicate complex mathematical ideas clearly.

How to apply

Apply through the 3Shape careers page using the application form linked in the posting. Prepare to submit your application materials via the online portal. Do not include photos or sensitive personal information in the application.

More information can be found here

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