Technical University of Denmark
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PhD Scholarship
PhD Scholarship in Machine Learning for Non-Stationary and Non-Static Nano-Tomography Technical University of Denmark in Denmark
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded PhD scholarship at DTU Compute. The appointment is for 3 years and follows the collective agreement with the Danish Confederation of Professional Associations; salary is according to DTU scientific staff salary structure. Research visits to Switzerland are planned as part of the project.
Deadline
Nov 16, 2026
Country
Denmark
University
Technical University of Denmark

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About this position
PhD Scholarship in Machine Learning for Non-Stationary and Non-Static Nano-Tomography at DTU Compute, Technical University of Denmark in Denmark.
This fully funded PhD project focuses on machine learning, deep learning, computational imaging, X-ray nano-tomography, inverse problems, neural representations, and geometric modeling. The research aims to develop novel learning-based methods for reconstructing accurate 3D and 4D representations from experimental imaging data while accounting for positional uncertainties, mechanical drift, and sample deformation during acquisition.
You will work on joint estimation of sample structure, acquisition geometry, and deformation fields from inconsistent projection data, with an emphasis on neural field representations, physics-informed reconstruction, and hybrid approaches combining learning-based methods with physical models. The project initially focuses on experimental nano-tomography data from integrated circuits.
The position is based at DTU Compute and is conducted in close collaboration with EPFL and the Paul Scherrer Institute (PSI) in Switzerland. The PhD student will be part of an international research environment spanning DTU, EPFL, and PSI, with planned research visits to Switzerland and access to advanced synchrotron imaging facilities.
Eligibility highlights: applicants should hold a two-year master's degree (120 ECTS) or equivalent in computer science, applied mathematics, physics, engineering, or a related field. A strong background in machine learning and deep learning is required, together with scientific programming experience (preferably Python), familiarity with ML frameworks, and solid knowledge of mathematical modeling, optimization, or numerical methods. Experience in neural representations, computer vision, inverse problems, image registration, deformable modeling, image processing, or tomographic reconstruction is an advantage. Good English communication skills are expected.
Funding and terms: the scholarship is fully funded and lasts 3 years. Salary and appointment terms follow the Danish collective agreement and DTU salary structure for scientific staff.
Application window: submit the complete online application no later than 2026-11-16. The application must be uploaded as one PDF containing a motivation letter, CV, and grade transcripts plus BSc/MSc diploma in English with grading scale description. You may apply before receiving the master's degree, but you cannot start before it is awarded.
Funding details
Fully funded PhD scholarship at DTU Compute. The appointment is for 3 years and follows the collective agreement with the Danish Confederation of Professional Associations; salary is according to DTU scientific staff salary structure. Research visits to Switzerland are planned as part of the project.
What's required
Applicants must have a master's degree or equivalent two-year master's degree (120 ECTS) in computer science, applied mathematics, physics, engineering, or a related discipline. A strong background in machine learning and deep learning is required, along with experience developing, implementing, or evaluating learning-based models. Candidates should have scientific programming experience, preferably in Python, familiarity with relevant machine learning frameworks, and a solid understanding of mathematical modeling, optimization, or numerical methods. Good written and spoken English is expected. Experience with neural representations, computer vision, inverse problems, image registration, deformable modeling, image processing, or tomographic reconstruction is an advantage; knowledge of X-ray imaging and tomography is beneficial but not required.
How to apply
Apply online via the 'Apply now' link and submit one PDF file with a motivation letter, CV, and grade transcripts plus BSc/MSc diploma in English with grading scale description. Applications must be submitted by the deadline and can be sent before the master's degree is obtained, but the degree must be received before starting.
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