PhD Scholarship in Machine Learning for Non-Stationary and Non-Static Nano-Tomography - DTU Compute

DTU - Technical University of Denmark; Kongens Lyngby; On-site; DKK 335,000 - 391,000; Full time

Job summary

DTU Compute invites applications for a fully funded PhD position focusing on learning-based methods for X-ray nano-tomography. The project integrates neural representations, inverse problems, and geometric modelling to reconstruct 3D/4D sample structures from noisy data.

You will collaborate with EPFL and PSI, visit Switzerland, and contribute to research-based teaching while publishing in leading journals. The position starts 1 March 2027 and runs for three years.

Qualifications

• Master's degree in computer science, applied mathematics, physics, engineering, or related discipline.
• Strong background in machine learning and deep learning with practical model development.
• Experience with scientific programming, preferably Python, and ML frameworks.

Responsibilities

• Develop novel ML methods for X-ray nano-tomography to jointly reconstruct structure, geometry, and deformation.
• Investigate neural field representations for 3D/4D imaging and hybrid physics-informed approaches.
• Validate methods with experimental nano-tomography data and share results.
• Collaborate with DTU, PSI and EPFL; participate in international research visits.”
• Publish results in ML, CV and computational imaging venues and contribute to teaching/supervision.

Skills

Machine learning

Deep learning

Python programming

Independent working

English communication

Education

Master's degree in CS, applied math, physics, engineering or related

Tools

Python

Job description

Job Description

Recent advances in machine learning and deep learning are opening new possibilities for computational imaging, including the reconstruction of complex 3D structures from experimental imaging data. 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, even minute positioning errors, mechanical drift, and sample deformations become sources of image degradation. The central challenge is to reconstruct accurate 3D and 4D representations of samples while accounting for positional uncertainties during image acquisition and changes in sample shape over time.

Recent advances in machine learning and deep learning are opening new possibilities for computational imaging, including the reconstruction of complex 3D structures from experimental imaging data. 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, even minute positioning errors, mechanical drift, and sample deformations become sources of image degradation. The central challenge is to reconstruct accurate 3D and 4D representations of samples while accounting for positional uncertainties during image acquisition and changes in sample shape over time.

The project will bring together neural representations, inverse problems, geometric modeling, and computational imaging to address fundamental challenges in nano-tomography. Rather than correcting acquisition errors and sample deformations as separate preprocessing steps, you will investigate how to model and estimate the sample structure, acquisition geometry, and deformation jointly from the measured data.

The position is based at DTU Compute and conducted in close collaboration with EPFL and the Paul Scherrer Institute (PSI) in Switzerland, one of the leading centers for synchrotron X-ray imaging. As a PhD student, you will work in an international research environment spanning DTU, EPFL, and PSI, combining expertise in both machine learning and computational imaging with access to experimental data and advanced imaging facilities. Research visits to Switzerland are planned as part of the project.

Responsibilities

• Develop novel machine learning and deep learning methods for X-ray nano-tomography, which jointly reconstruct sample structure, acquisition geometry, and deformation fields from inconsistent projection data.


• Investigate neural field representations for 3D and 4D imaging, including models that capture changes in sample geometry over time, and explore hybrid approaches combining learning-based methods with physical models of image formation and tomographic reconstruction.


• Validate and evaluate the developed methods using experimental nano-tomography data, initially focusing on integrated circuits.


• Collaborate with researchers at DTU, PSI, and EPFL, and participate in international research visits and scientific activities.


• Publish research results in leading journals and conferences in machine learning, computer vision, and computational imaging.


• Contribute to research-based teaching and supervision of MSc and BSc student projects.


• Participate in activities within the Center for Quantification of Imaging Data from MAX IV (QIM), including workshops, training courses, and interdisciplinary research collaborations.

Qualifications

You are curious and motivated by developing new machine learning methods to solve challenging problems in computational imaging. A strong background in machine learning and deep learning is particularly important. We welcome candidates from computer science, applied mathematics, physics, engineering, and related disciplines who are interested in combining modern learning methods with mathematical modeling and real-world experimental data. The candidate should be able to work independently, contribute effectively to an interdisciplinary research team, and communicate scientific ideas clearly in written and spoken English.

To succeed in this position, you are expected to have:

• A master's degree in computer science, applied mathematics, physics, engineering, or a related discipline.


• A 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.


• A solid understanding of mathematical modeling, optimization, or numerical methods.

Experience in one or more of the following areas is an advantage:

• Neural representations, implicit neural fields, or other coordinate-based representations.


• Computer vision, 3D vision, geometric modeling, or structure-from-motion.


• Inverse problems, image reconstruction, or physics-informed machine learning.


• Image registration, motion estimation, or deformable modeling.


• Image processing, image analysis, or tomographic reconstruction.


• Knowledge of X-ray imaging and tomography is beneficial but not required.

You must have 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.

Approval and Enrolment

The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education.

We offer

DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

At DTU Compute, we have a large focus on creating a good working environment. Our work with diversity and inclusion is an essential part of that. We believe that having a good working environment is a necessary foundation to provide excellent research, teaching, supervision and innovation.

At DTU Compute, we have about 140 PhD students. We offer several events and activities for all our PhD students such as annual PhD Bazaar, onboarding course where you will meet other PhD students, and you can join our PhD Club that organizes BBQs, board games events, lunches etc.

Salary and appointment terms

The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. Please see DTU’s salary structure for scientific staff: www.inside.dtu.dk/en/human-resources/during-employment/salary/salary-structures.

The period of employment is 3 years. The starting date is 1 March 2027 (or according to mutual agreement). The position is a full-time position.

You can read more about career paths at DTU here.

Further information

Further information may be obtained from Hans Martin Kjer, [e-mail].

You can read more about DTU Compute at www.compute.dtu.dk.

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark. Furthermore, you have the option of joining our monthly free seminar \"PhD relocation to Denmark and startup \"Zoom\" seminar\" for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU.

All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply. As DTU works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be conducted on qualified candidates for the position.

DTU Compute

DTU Compute – Department of Applied Mathematics and Computer Science – is an internationally recognised academic environment with over 400 employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus on ensuring that our research results contribute to creating a better society by supporting areas such as health, green transition, energy supply, and life science. We collaborate with universities, public and private organisations, and companies in Denmark and abroad, and through DTU’s startup ecosystem, we encourage innovation and entrepreneurship. We have a strong ethical, human, and sustainable approach that ensures integrity in our work. Therefore, we strive for and take responsibility for driving the democratisation of digital technologies, so that everyone has the opportunity to actively participate in the development, and we ensure a continued open, democratic, and inclusive society for the benefit of all. At DTU Compute, we value diversity, inclusion, and a flexible work-life balance. Read more here: www.compute.dtu.dk.

DTU - For the benefit of society since 1829

DTU is one of Europe's leading elite technical universities. Through research and education at an international top level, we create solutions to the major societal challenges of our time and help secure Europe's global leadership in sustainable technological development. Since Hans Christian Ørsted founded DTU almost 200 years ago, our mission has remained the same: We develop and create value through the natural and technical sciences for the benefit of society. DTU has 13,800 students, 1,600 PhD students, and 6,500 employees. We work in an international environment and have an inclusive, stimulating, and informal work culture. DTU has campuses in all parts of Denmark and in Greenland and collaborates with the best universities around the world.