The safe, economical, and transparent management of radioactive waste requires robust classification methods that support disposal route selection and regulatory compliance. A particularly important challenge is the distinction between low-level waste (LLW) and very low-level waste (VLLW), since the boundary between these categories may strongly influence treatment, conditioning, transport, storage, and disposal costs. In many cases, waste packages are classified conservatively because the available characterization methods are limited, slow, or insufficiently informative for heterogeneous waste forms. This may result in waste packages being assigned to a higher category than necessary. Momentarily, in Hungary the overwhelming majority of the radioactive waste is handled as low/intermediate waste and it is taken to the Bátaapáti repository site, approximately 250 m below ground level. On the other hand, there are plans to construct a VLLW disposal site in the near future.

The proposed PhD research aims to develop an advanced methodology for the characterization and classification support of radioactive waste drums, with special focus on identifying packages that may meet VLLW acceptance criteria.

The first objective of the research is a comparative analysis of LLW and VLLW classification criteria and acceptance requirements in different countries. This task will review national regulations, disposal concepts, radionuclide limits, measurement expectations, and decision practices, with the goal of identifying common principles and key differences that influence classification. This international benchmarking will provide the regulatory and technical basis for a generalized decision-support framework.

The second objective is the development of an experimental characterization tool for waste packages. The research will investigate how non-destructive assay methods — including gamma spectrometry, spectral imaging, surface and volumetric scanning, and possibly X-ray or computed tomography (CT) — can provide relevant information on activity distribution, radionuclide composition, matrix heterogeneity, density structure, and the presence of localized hot spots. A central research question is which metric can best characterize and distinguish waste packages that are likely compatible with VLLW criteria from those requiring LLW classification. The work will consider realistic waste drums with mixed material composition and non-uniform source distributions, reflecting practical waste management conditions.

The experimental programme will be supported by Monte Carlo simulations, which will play a key role in detector design, measurement optimization, uncertainty analysis, and virtual scenario generation. Monte Carlo models will be used to study radiation transport in heterogeneous drum geometries, to evaluate detector responses under different source and shielding configurations, and to optimize measurement arrangements for sensitivity and classification reliability. Simulation results will also help generate synthetic datasets for training and testing advanced data evaluation methods.

A further key element of the PhD is the application of deep learning and data-driven analysis. Machine learning models will be developed to interpret spectral, scanning, and imaging data and to support classification decisions. These methods may enable the recognition of complex patterns that are difficult to identify using conventional analysis alone, especially in the case of heterogeneous waste matrices. The combined use of experiments, simulation, and artificial intelligence is expected to provide a practical methodology that is applicable in practice.

The expected outcome of the research is a scientifically validated framework for supporting the distinction between LLW and VLLW waste packages, including recommendations for measurement system design, data analysis workflow, and decision criteria. The results may contribute to improved waste characterization practice, reduced disposal costs, and more optimized use of disposal capacity, while maintaining a high level of radiological safety and regulatory confidence.