About the opportunity
In this fully funded PhD project at DTU Compute, you will develop novel learning-based methods for X-ray nano-tomography. As state-of-the-art synchrotron imaging systems push towards ever higher spatial resolution, mechanical drift and sample deformations cause image degradation. The project brings together neural representations, inverse problems, geometric modeling, and computational imaging to jointly estimate sample structure, acquisition geometry, and deformation fields from inconsistent projection data. Based at DTU Compute in Kgs. Lyngby, Denmark, the position involves close collaboration with EPFL and the Paul Scherrer Institute (PSI) in Switzerland, including planned international research visits. You will join an environment with approximately 140 PhD students and contribute to research-based teaching.
Salary and benefits
- Fully funded 3-year PhD position based on the collective agreement with the Danish Confederation of Professional Associations
- Salary and appointment terms in accordance with DTU's salary structure for scientific staff
- International collaborative research environment spanning DTU, EPFL, and PSI with planned research visits to Switzerland
- Access to experimental data, advanced synchrotron imaging facilities, and participation in the Center for Quantification of Imaging Data from MAX IV (QIM)
- Supportive PhD community featuring onboarding courses, the PhD Club, and relocation support seminars
Who can apply?
- A two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree in computer science, applied mathematics, physics, engineering, or a related discipline
- Strong background in machine learning and deep learning, including experience developing, implementing, or evaluating learning-based models
- Experience with scientific programming, preferably in Python, and familiarity with relevant machine learning frameworks
- Solid understanding of mathematical modeling, optimization, or numerical methods
- Ability to work independently, contribute effectively to an interdisciplinary team, and communicate clearly in written and spoken English
Required documents
- Cover letter motivating the application
- Curriculum vitae (CV)
- Grade transcripts and BSc/MSc diploma in English, including official description of the grading scale
When to apply
- Application deadline: 16 November 2026 at 23:59 Danish time
- Expected starting date: 1 March 2027 (or according to mutual agreement)
How to apply
- Prepare all required application materials in English and merge them into a single PDF file
- Open the online application portal via the official Apply Now link on the job posting
- Fill out the online application form and upload your single PDF file containing all credentials before the deadline
Additional information
- Candidates may apply prior to obtaining their master's degree, but cannot begin before receiving it
- As DTU works with research in critical technology subject to special rules for security and export control, open-source background checks may be conducted on qualified candidates
Our Take on This Opportunity
This section reflects Open Opportunities’ own analysis of the opportunity from an applicant’s perspective.
You may be a strong fit if…
- An ideal match for researchers with a strong foundation in machine learning, deep learning, and mathematical modeling who want to apply AI to cutting-edge physical sciences.
- Well-suited for candidates excited about interdisciplinary teamwork, given the active collaboration structure between DTU, EPFL, and PSI.
- Provides an excellent stepping stone for an academic or advanced research career through high-impact publishing opportunities and international exposure.
What should you think carefully about?
- Relocation to Denmark is required for the duration of the 3-year appointment, alongside temporary research visits to Switzerland.
- Requires meeting rigorous academic enrollment standards and passing potential security and export control background checks.
- Fixed-term 3-year commitment with specific deliverables including dissertation completion and research-based teaching duties.
How could this fit your current goals and plans?
This scholarship fits exceptionally well if your goal is to pursue a rigorous, fully funded European PhD at the intersection of AI, computer vision, and computational imaging without funding uncertainties. It is designed to build deep technical expertise, international institutional networks, and publication records valuable for both academic and industrial research paths.
Our take
Master's graduates in computer science, applied mathematics, physics, or engineering with a passion for machine learning and scientific imaging should strongly consider applying before the November 2026 deadline. It is less suitable for candidates seeking immediate industry employment or those unable to relocate to Denmark.
Ready to apply?
Review the opportunity on the official website before applying.
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