Doctoral fellow - Department of Telecommunications and Information Processing

Technology » Telecommunications technology

Organisation/Company Ghent University Research Field Computer science » Programming Computer science » Other Engineering » Other Mathematics » Algebra Mathematics » Computational mathematics Technology » Telecommunications technology Researcher Profile First Stage Researcher (R1) Final date to receive applications 13 Feb 2026 - 22:59 (UTC) Country Belgium Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme?

Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Department TW07 - Department of Telecommunications and Information Processing

Degree
• You hold a Master's degree (preferably) in Engineering, Physics, Mathematics, Computer Science, or an equivalent degree. The degree requirements must be met by the date of appointment.

Occupancy rate 100%

Vacancy type Research staff

ABOUT GHENT UNIVERSITY

Ghent University is a world of its own. Employing more than 15,000 people, it is actively involved in education and research, management and administration, as well as technical and social service provision on a daily basis. It is one of the largest, most exciting employers in the area and offers great career opportunities.

With its 11 faculties and more than 85 departments offering state‑of‑the‑art study programmes grounded in research in a wide range of academic fields, Ghent University is a logical choice for its staff and students.

Image Processing and Interpretation (IPI, ) is an imec research group at Ghent University. IPI consists of 40 researchers and conducts state‑of‑the‑art research in digital image and video processing for a wide range of applications, including real‑time image and video processing and machine learning. This encompasses application domains such as industrial inspection, (ultra) high‑definition video enhancement, smart multi‑camera networks, computer vision, sensor fusion, and (medical) imaging.

This project is in close collaboration with the Radiation Physics research group at the Center for X‑ray Tomography (UGCT, ). This group conducts research on micro‑CT, an imaging technique for examining materials and objects at high resolution in 3D.

This vacancy concerns a PhD‑level research position within an ambitious project on the reconstruction of micro‑CT using machine learning in the form of implicit neural representations (INRs). The goal is to improve traditional computed tomography by avoiding computational bottlenecks by directly reconstructing and analyzing raw μCT data into a compressed neural form, rather than via the intermediate step of traditional discrete voxel grids.

You will conduct fundamental and applied research at the intersection of linear algebra, inverse problems, signal processing, and machine learning. The position requires close collaboration with experts in both image processing and imaging physics, as well as with researchers in computational mathematics and applied AI. In addition to the collaboration within Ghent University, there is also a collaboration with the Centrum Wiskunde en Informatica (CWI, http://(Use the "Apply for this Job" box below).),

a research institution in Amsterdam specialising in computational mathematics.

• You hold a Master's degree (preferably) in Engineering, Physics, Mathematics, Computer Science, or an equivalent degree. The degree requirements must be met by the date of appointment.
• A strong interest in signal processing, inverse problems, and mathematics is essential for success in this position, in addition to expertise in machine learning.
• Familiarity with (medical) image reconstruction (tomography) or computational imaging is highly desirable.
• Knowledge of and experience with machine learning is also a plus. You will design, implement, and validate novel algorithms, and benchmark them against state‑of‑the‑art reconstruction pipelines.
• Strong programming skills (e.g., Python/C++ and GPU‑based computing) and proficiency with modern machine learning frameworks are required.
• We seek a motivated researcher eager to push the boundaries of dynamic imaging through the…