PhD Scholarship: ML for X-ray Nano-Tomography

DTU - Technical University of Denmark; Kongens Lyngby; Vor Ort; DKK 335.000 - 391.000; Vollzeit

Zusammenfassung

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.

Qualifikationen

• 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.

Aufgaben

• 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.

Kenntnisse

Machine learning

Deep learning

Python programming

Independent working

English communication

Ausbildung

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

Tools

Python

Jobbeschreibung

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.