PhD in Advanced Tomography Imaging for Evaluating Radioactive Waste Conformity for Safe Final Disposal Belgian Nuclear Research Centre in Belgium

The Belgian Nuclear Research Centre (SCK CEN) is offering a fully funded PhD position focused on advanced tomography imaging for evaluating radioactive waste conformity, supporting safe final disposal in Belgium. This research addresses the critical challenge of characterizing nuclear legacy waste, much of which was conditioned under outdated practices and now requires verification against modern waste acceptance criteria (WAC). The recent licensing of the cAt facility for near-surface disposal of low- and intermediate-level short-lived radioactive waste has created an urgent need to assess approximately 70,000 m³ of conditioned waste, primarily stored in 400-liter drums.

Significant uncertainties remain regarding the chemical composition, packaging integrity, and compliance with WAC, especially for components such as cellulose, chlorides, and sulphates. Non-destructive examination (NDE) methods are essential to close these information gaps while maintaining package integrity. Conventional radiographic techniques are insufficient for dense, heterogeneous waste matrices. This project leverages high-energy (megavolt, MV) computed tomography (CT) using a linear accelerator (LINAC) to enable three-dimensional visualization and, with dual-energy CT (DECT), material discrimination and compositional analysis.

The PhD research will establish a non-destructive MV-CT workflow for historical waste characterization at SCK CEN’s Laboratory of Nuclear Calibrations (LNK). Key innovations include the development of physics-informed reconstruction algorithms, integration of simulation and AI-based analysis, and the use of an analytical Monte Carlo-inspired simulator to optimize system geometry and acquisition parameters. This simulator will support sensitivity studies and serve as a forward projector in image reconstruction. Dual-energy imaging will be explored for quantitative determination of electron density and effective atomic number, providing indicators relevant to WAC. Machine learning techniques will be applied for artifact correction, segmentation, and material classification.

By combining experimental imaging, simulation, and data-driven interpretation, the project aims to deliver high-resolution, reliable characterization of conditioned waste drums, reducing uncertainties and supporting safe, compliant disposal in Belgium’s near-surface repository. The successful candidate will benefit from SCK CEN’s multidisciplinary research environment, close cooperation with the academic sector, and access to innovative facilities designed for nuclear experiments. The SCK CEN Academy offers excellent internal training courses and opportunities for professional development.

Eligibility: Applicants must hold a Master of Science degree or equivalent in Physics, Mathematics, or Engineering, with a strong background in physics and mathematics. Excellent English language skills are required. The position is full-time, with an estimated duration of 4 years.

Funding: The PhD position is fully funded, including a stipend and benefits as provided by SCK CEN Academy. Details on funding and benefits are available via the SCK CEN website.

Application Window: Applications are open until 31 March 2026. The position starts on 1 October 2026.

How to Apply: Interested candidates should apply online via the SCK CEN website, submitting a CV, motivation letter, and relevant academic documents before the deadline. For further information, visit the position and application links or contact [email protected].