PhD in ML for Dynamic Nano-Tomography & 4D Imaging

Technical University of Denmark; Kongens Lyngby; On-site; DKK 320,000 - 420,000; Full time

Benefits offered by this job

Research visits abroad

Collaboration with EPFL/PSI

Job summary

Technical University of Denmark (DTU) invites applications for a fully funded PhD project in DTU Compute. You will develop learning-based methods for X-ray nano-tomography, addressing reconstruction under geometric and deformation uncertainties.

The role blends machine learning with inverse problems and physics-informed imaging. Work in an international setting with DTU, EPFL, and PSI, including planned research visits.

Qualifications

• Master's degree in computer science, applied mathematics, physics, engineering, or related discipline.
• Strong background in machine learning and deep learning; experience developing or evaluating learning-based models.
• Experience with scientific programming, preferably Python; familiarity with ML frameworks.
• Solid understanding of mathematical modeling, optimization, or numerical methods.

Responsibilities

• Develop ML and DL methods for X-ray nano-tomography to jointly reconstruct sample structure, geometry, and deformation fields.
• Explore neural field representations for 3D/4D imaging and hybrid learning-physics approaches.
• Validate methods on experimental nano-tomography data, starting with integrated circuits.
• Collaborate with DTU, PSI, and EPFL; participate in international visits and conferences.
• Publish results in top ML, CV, and imaging venues.

Skills

Machine learning

Deep learning

Python

Numerical methods

Computer vision

Education

Master's degree in CS/Math/Engineering

Tools

PyTorch

NumPy

Job description

Technical University of Denmark (DTU) invites applications for a fully funded PhD project in DTU Compute. You will develop learning-based methods for X-ray nano-tomography, addressing reconstruction under geometric and deformation uncertainties.

The role blends machine learning with inverse problems and physics-informed imaging. Work in an international setting with DTU, EPFL, and PSI, including planned research visits.